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## Material Information

Title:
Bayesian modeling of nonstationarity in normal and lognormal processes with applications in CVP analysis and life testing models
Creator:
Velez-Arocho, Jorge Ivan, 1947-
Publication Date:
1978
Language:
English
Physical Description:
xiii, 24 leaves : graphs ; 28 cm.

## Subjects

Subjects / Keywords:
Gaussian distributions ( jstor )
Mathematical independent variables ( jstor )
Modeling ( jstor )
Parametric models ( jstor )
Random variables ( jstor )
Sample mean ( jstor )
Statistical discrepancies ( jstor )
Statistical estimation ( jstor )
Statistical models ( jstor )
Statistics ( jstor )
Bayesian statistical decision theory ( lcsh )
Break-even analysis ( lcsh )
Dissertations, Academic -- Management -- UF ( lcsh )
Economic forecasting -- Mathematical models ( lcsh )
Management thesis Ph. D ( lcsh )
Genre:
bibliography ( marcgt )
non-fiction ( marcgt )

## Notes

Thesis:
Thesis--University of Florida.
Bibliography:
Bibliography: leaves 198-212.
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by Jorge Ivan Velez-Arocho.

## Record Information

Source Institution:
University of Florida
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University of Florida
Rights Management:
Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Resource Identifier:
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04536074 ( OCLC )
AAH6803 ( NOTIS )

Full Text

BAYESIAN MODELING OF NONSTATIONARITY IN
NORMAL AND LOGNORMAL PROCESSES WITH
APPLICATIONS IN CVP ANALYSIS AND
LIFE TESTING MODELS

By

JORGE IVAN VELEZ-AROCHO

A DISSERTATION PRESENTED TO THE GRADUATE COUNCIL OF
TIE UNIVERSITY OF FLORIDA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHItLISOPIY

UNIVERSITY OF FLORIDA

1978

by

Jorge Ivan Velez-Arocho

This dissertation stands as a symbol of love
to my wife, Angle, and to my daughter,
Angeles Maria, without whose under-
standing, patience and willingness
to accept sacrifice this
investigation would have
been quite impossible.

ACKNOWLEDGMENTS

I would like to acknowledge my full indebtedness to those

people who gave their interest, time and effort to making this dis-

sertation possible.

To Dr. Christopher B. Barry who has been my advisor and my

friend, I wish to express my gratitude and deepest appreciation for the

support he has given me throughout the development of this study. He

critized but tolerated my mistakes and encouraged my good performance.

His intelligent guidance, extraordinary competence, and friendly attitude

have been a source of inspiration and encouragement for me.

I am especially grateful to Dr. Antal Majthay for his sincere

advice and assistance during the supervision of my doctoral program and

the preparation of this dissertation. I admire and am inspired by his

unreserved dedication to excellence in education. He will always be

remembered as one of the most valuable models of excellent teaching.

The other members of my committee, Dr. Tom Hodgson and Dr. Zoran

Pop-Stojanovic have each in his own way contributed to the successful

completion of this work. Appreciation is extended to each for his indi-

vidual efforts and expressed concern for my progress. Although not on

my committee, I would also like to express appreciation to Dr. Gary

Koehler, whose support and encouragement came when they were badly

needed.

of the University of Pucurt.o Rico at Mayaguez, I am particularly grateful

i v

for his understanding, confidence and cooperation during my leave of

absence from that institution. Completion of this study was only pos-

sible because of the combined financial support of the University of

Puerto Rico, the University of Florida and Peter Eckrich and Sons Co..

'li'h ii ouil in u iou support is sincerely appreciated.

I am indebted to Dr. Conrad Doenges, Chairman of the Department

of Finance of the University of Texas at Austin, for his interest and

help and to the many members of the Finance faculty for their interest

during my period of research at the University of Texas. Special thanks

go to Nettie Webb for her warm friendship and continuous secretarial

assistance to my wife.

It is difficult to adequately convey the support my family has

provided. My parents, Jorge Velez and Elba Iucrecia Arocho, and my

brothers and sisters provided understanding and moral assistance for

which I will always be grateful. Their high expectations and constant

encouragement have been a powerful factor in shaping my desire to pursue

this degree.

Most of all a gratitude which cannot be expressed in words

goes to my loving wife, Angle, for her patience and persistence in

typing this dissertation and for her wonderful attitude throughout

the entire arduous process.

ACKNOWLEDGEMENTS .................

LIST OF APPENDIX TABLES..........

LIST OF FIGURES ..................

ABSTRACT..........................

Chapter

ONE INTRODUCTION .............................................

1.1 Introduction ........................................
1.2 Summary of Results and Overview of Dissertation.....

TWO SURVEY OF PERTINENT LITERATURE...........................

2.1 Cost-Volume-Profit (CVP) Analysis...................
2.2 Life Testing Models .................................

2.2.1 Introduction.................................
2.2.2 Some Common Life Distributions..............
2.2.3 Traditional Approach to Life Testing
Inferences ..................................
2.2.4 Bayesian Techniques in Life Testing.........

2.3 Modeling of Nonstationary Processes.................

THREE NONSTATLONARITY IN NORMAL AND LOGNORMAL PROCESSES........

3.1 Introduction.........................................
3.2 Bayesian Analysis of Normal and Lognormal Processes.
3.3 Nonstationary Model for Normal and Lognormal Means..

.33.1 i is Unknown and a2 is Known................
3.3.2 j and d2 Both Unknown.......................
3.3.3 Stationary Versus Nonstationary Results.....

3.4 Conclusion ..........................................

FOUR LIMITING RESULTS AND PREDICTION INTERVALS FOR NONSTA-
TTONAKY NORMAL AND LOGNORIALI PROCESSES ...................

4 .1 Lnt roduc t ion ........................................

vi

Page

iv

ix

x

xi

..........

. . . . . .

..........

..........

....................

....................

....................

....................

4.2 Special Properties and Limiting Results Under
Nonstationarity. ........................................ 86

4.2.1 Limiting Behavior of m' and n' When P is
the Only Unknown Parameter..................... 86
4.2.2 Limiting Behavior of m nt', v and dt
When Both Parameters p and oc are Unknown....... 95

4.3 Prediction Intervals for Normal, Student, Lognormal
and LogStudent Distributions............................ 103
4.4 Conclusion. ........................................... 117

FIVE NONSTATIONARITY IN CVP AND STATISTICAL LIFE ANALYSIS........ 119

5.1 Introduction........................................... 119
5.2 Nonstationarity in Cost-Volume-Profit Analysis.......... 120

5.2.1 Existing Analysis............................... 120
5.2.2 Nonstationary Bayesian CVP Model ............... 122
5.2.3 Extensions to the Nonstationary Bayesian
CVP Model...................................... 136

5.3 Nonstationarity in Statistical Life Analysis........... 140

5.3.1 Existing Analysis............................... 140
5.3.2 A Life Testing Model Under Nonstationarity..... 141

5.4 Conclusion............................................. 148

SIX CONCLUSIONS, LIMITATIONS AND FURTHER STUDY.................. 150

6.1 Summary ............. ........................... 150
6.2 Limitations ............................. ........ 152
6.3 Suggestions for Further Research ............... 155

APPENDIXES

I Bayesian Analysis of Normal and Lognormal Processes......... 160
IT Nonstationary Models for the Exponential Distribution....... 172
IIL Algorithm to Determine Prediction Intervals for Lognormal
and LogStudent Distributions ................................ 185

Chapter

Page

Page

LIST OF REFERENCES.................................................... 198

BIOGRAPHICAL SKETCH .................................................... 213

viii

LIST OF APPENDIX TABLES

Table Page

1. predictive Intervals for Some Lognormal Predictive
Distributions ........................................ .... 191

2. Predictive Intervals for Some LogStudent Predictive
Distributions. ............................................ 192

LIST OF FIGURES

Figure Page

1. Life Characteristics of Some Systems...................... 21

AIII.1 Predictive Distribution.................................. 186

AIII.2 Predictive Distribution .................................. ]87

AIII.3 Predictive Distribution.................................. 188

AIII.4 Predictive Distribution.................................. 189

Abstract of Dissertation Presented to the Graduate Council
of the University of Florida in Partial Fulfillment of the Requirements
for the Degree of Doctor of Philosophy

BAYESIAN MODELING OF NONSTATIONARITY IN
NORMAL AND LOGNORMAL PROCESSES WITH
APPLICATIONS IN CVP ANALYSIS AND
LIFE TESTING MODELS

By

Jorge Ivan Velez-Arocho

June 1978

Chairman: Christopher B. Barry
Major Department: Management

Probability models applied by decision makers in a wide variety

of contents must be able to provide inferences under conditions of change.

A stochastic process whose probabilistic properties change through time

can be described as a nonstationary process. In this dissertation a model

involving normal and lognormal processes is developed for handling a par-

ticular form of nonstationarity within a Bayesian framework. Two uncer-

tainty conditions are considered; in one the location parameter, p, is

assumed to be unknown and the spread parameter, a, is assumed to be known;

and in the other both parameters are assumed to be unknown. Comparing the

nonstationary model with the stationary one it is shown that:

_1. more uncertainty (of a particular definition) is present

under nonstationarity than under stationarity;

2. since the variance of a lognormal distribution, V(x), is a

function of p and o2, nonstationarity in P means that both mean and vari-

ance of the random variable, x, are nonstationary so that the lognormal

xi

case provides a generalization of the normal results;

and

stochastically-varying parameters is never entirely eliminated.-

The asymptotic behavior of the model has important implications

for the decision maker. An implication of the stationary Bayesian model

for normal and lognormal processes is that as additional observations are

collected,parameter uncertainty is reduced and (in the limit) eliminated

altogether. In contrast, for the nonstationary model considered in this

dissertation the following inferential results are obtained:

1. for the case of lognormal or normal model, a particular form

of stochastic parameter variation implies a treatment of data involving

the use of all observations in a differential weighting scheme;

and

2. random parameter variation produces important differences in

the limiting behavior of the prior and predictive distributions since

under nonstationarity the limiting values of the parameters of the poste-

rior and predictive distributions cannot be determined clearly.

Practical implications of the results for the areas of Cost-

Volume-Profit Analysis and life testing are discussed with emphasis on

the predictive distribution for the outcome of a future observation from

the data generating process. It is emphasized that a Cost-Volume-Profit

(CVP) and life testing model ideally should include the changing charac-

ter of the process by allowing for changes in the parametric description

of the process through time. Failure to recognize nonstationarity when

Xii

it is present has a number of implications in the CVP and life-testing

contexts that are explored in the dissertation. For example, inferences

are improperly obtained if the nonstationarity is ignored, and prediction

interval coverage probabilities are overstated since uncertainty is

greater (in a particular sense) when nonstationarity is present.

:'7 A

CHAPTER ONE

INTRODUCTION

1.1 Introduction

Uncertainty is an essential and intrinsic part of the human

condition. The opinions we express, the conclusions we reach and the

decisions we make are often based on beliefs concerning the probability

of uncertain events such as the result of an experiment, the future value

of an investment or the number of units to be sold next year. If manage-

ment, for instance, were certain about what circumstances would exist at

a given time, the preparation of a forecast would be a trivial matter.

Virtually all situations faced by management involve uncertainty, however,

and judgments must be made and information must be gathered to reduce

this uncertainty and its effects. One of the functions of applied mathe-

matics is to provide information which may be used in making decisions

or forming judgments about unknown quantities.

Several early studies by econometricians and statisticians

examined the problem of constructing a model.whose output is as

close aspossible to the observed data from the real system and which

reflects all the uncertainty that the decision maker has. Mathematical

models for statistical problems, for instance, have some element of un-

certainty incorporated in the form of a probability measure. The model

usually involves the formulation of a probability distribution of the

uncertain quantities. This element of uncertainty is carried through

the analysis to the inferences drawn. The equations that form the mathe-

matical model are usually specified to within a number of parameters

or coefficients which must be estimated. The unknown parameters are

usually assumed to be constant and the problem of model identification

is reduced to one of constant parameter estimation.

There are several reasons for suspecting that the parameters

of many models constructed by engineers and econometricians are not

constant but in fact time-varying. For instance, it has become increas-

ingly clear that to assume that behavioral and technological relationships

are stable over time is,in many cases,completly untenable on the basis

of economic theory. Several recent studies provide support for the claim

that the parameters of distributions of stock-price-related variables may

change over time [see Barry and Winkler (1976)]. In engineering, particu-

larly in reliability theory, the origins of parameter variation are usually

not very hard to pinpoint. Component wear, variation in inputs or compo-

nent failure are some very common reasons for parameter variations. The

major objective of construction of engineering models is control and regu-

lation of the real system modeled. Therefore, much of the research in

that area has concentrated on devising ways to make the output of the

model insensitive to parameter variation. Similarly, in forecasting models

for economic variables, researchers have had great concern with time varying

parameters of the distributions of interest. In this area the problem of

varying parameters has received increased attention because there is

increasing evidence that the common regression assumption of stable

parameters often appears invalid.

Ln this dissertation we plan to study a particular type of random

parameter variation which is likely to be applicable when nonstationariLy

over time is present. The modeling of nonstationarity that we are going to

present assumes that successive values in time of the unknown parameter

are related in a stochastic manner; i.e., the parameter variation includes

a component which is a realization of some random process. For purposes

of estimation we are interested in specific realizations of the random

process. When the process generating the unknown parameter is a nonsta-

tionary process over time the decision maker should be concerned with

a sequence of values of the parameter instead of a single value as in

the usual stationary model; i.e., inferences and decisions concerning the

parameter should reflect the fact that it is changing over time.

If the values of an unknown parameter over time are related

in a stochastic manner, a formal analysis of the situation requires

some assumptions about the stochastic relationship. For the model of

nonstationarity that we develop in this dissertation, the specification

of the stochastic relationship between values of the parameter is suf-

ficient. Moreover it is assumed that this relationship is stationary

(usually referred to as second-order stationarity) in the sense that the

stochastic relationship is the same for any pair of consecutive values

of the unknown parameter.

We want to gain more precise information about the structure

of the time-varying parameters and to obtain estimated relationships

that are suitable for forecasting. The model to be developed makes it

possible to draw inferences about the structure of the relationship at

every point in time. There are problems in accounting, life testing theory,

finance and a variety of other areas that can benefit from nonstationary

parameter estimation techniques.

1.2 Summary of Results and Overview of Dissertation

The goals of this dissertation are to develop a rigorous model

for handling nonstationarity within a Bayesian framework, to compare

inferences from stationary and nonstationary models, and to investigate

inferential applications in the areas of Cost-Volume-Profit Analysis and

life testing models involving nonstationarity. Probably the most important

advantage of the new work to be presented in this dissertation is the

increased versatility it adds to the nonstationary Bayesian model derived

by Winkler and Barry (1973). The new results enlarge the range of real and

important problems involving univariate and multivariate nonstationary

normal and lognormal processes which can be handled. Another advantage

is the simplicity of the updating methods for the efficient handling of

the estimation of unknown parameters and the prediction of the outcome

of a future sample.

A survey of the most relevant literature is provided in Chapter

Two to set the stage for the new developments in the remainder of the dis-

sertation. In this survey we present an overview of probabilistic Cost-

Volume-Profit (CVP) Analysis and discuss the most important articles

that deal with CVP under- conditions of uncertainty. The rev t~w of the

literature includes a section on life testing models emphasizing the use

of Bayesian techniques used in life testing. It is emphasized that most

of the research done in these two areas neglects the problem of nonsta-

tionarity. A special section is presented to discuss some important

As is mentioned in Chapter Two, most research concerned with

the normal and lognormal distributions has considered only stationary

situations. That is, the parameters and distributions used are assumed

to remain the same in all periods. In Chapter Three we develop a Bayesian

model of nonstationnrity for normal and lognormal processes. In it we

describe essential features of the Bayesian analysis of normal and log-

normal processes inder nonstationarity, like the prior, posterior and

predictive distributions. Two uncertainty conditions are considered in

this chapter; in one the location parameter, 1, is assumed to be unknown

and the spread parameter, o, is assumed to be known; and in the other,

both parameters are assumed to be unknown. Comparing the nonstationary

model with the stationary one it is shown that:

1. more uncertainty (of a particular definition) is present

under nonstationarity than under stationarity;

2. since the variance of a lognormal distribution, V(x), is a

function of p and (J2 nonstationarity in p means that both mean and vari-

ance of the random variable, x, are nonstationary, so that the lognormal

case provides a generalizati n of the normal results;

and,

stochastically-varying parameters is never entirely eliminated.

The results discussed in Chapter Three have to do with the period-

to-period effects of random parameter variation upon the posterior and pre-

dictive distributions. However, the asymptotic behavior of the model has

important implications for the decision maker. An implication of the sta-

tionary Bayesian model for normal and lognormal processes is that as addi-

tional observations are collected parameter uncertainty is reduced and

(in the limit) eliminated altogether. Such an implication is inconsistent

with observed real world behavior largely because the conditions under

which inferences are made typically change across time. The common dictum

[see Dickinson (1974)] has been to eliminate some observations in the case

of changing parameters so that only those most recent observations are

considered. In Chapter Four we show that:

1. for the case of a lognormal or normal model, a particular

form of stochastic parameter variation implies a treatment of data

involving tile use of all observations in a differential weighting scheme,

and,

2. random parameter variation produces important differences

in the limiting behavior of the prior and predictive distributions since

under nonstationarity tile limiting values of some of the parameters of the

posterior .nd predictive distributions can not be determined clearly.

One objective of tlis dissertation is to develop Bayesian pre-

diction intervals for future observations that come from normal and log-

normal data generating processes. In Chapter Four we address the problem

of constructing prediction intervals for normal, Student, lognormal and

logStudent distributions. It is pointed out that it is easy to construct

these intervals for the normal and Student distributions but that it is

rather difficult for the lognormal and logStudent distributions. An

algorithm is presented to compute the Bayesian prediction intervals for

the lognormal and logStudent distributions. Bayesian prediction intervals

under nonstationarity are compared with classical, certainty equivalent

and Bayesian stationary intervals.

In Chapter Five we discuss the application of the results of

Chapters Three and Four concerning nonstationarity to the area of CVP

analysis and life testing models. Practical implications of our results

for these two areas are discussed with emphasis on the predictive dis-

tribution for the outcome of a future observation from the data generating

process. It is emphasized that CVP and life testing models ideally

should include the changing character of the process by allowing for

changes in the parametric description of the process through time. It

is shown that, for the case of normal and lognormal data generating

processes under a particular form of stochastic parameter variation, the

presence of nonstationarity produces greater uncertainty to the decision

maker. Nonstiationarilty implies greater uncertainty, which is reflected

by an increase in lie predictive variance of profits for CVP models,

8

by an increase in the predictive variance of life length for life testing

models, and by an increase in the width of intervals required to contain

particular coverage probabilities.

Chapter Six provides conclusions, limitations and suggestions

for further research. Since stationarity assumptions are often quite

unrealistic, it is concluded in that chapter that the introduction of

possible nonstationarity greatly increases the realism and applicability

of statistical inference methods, in particular of Bayesian procedures.

CHAPTER TWO

SURVEY OF PERTINENT LITERATURE

The primary purpose of the research in this dissertation is

to present a Bayesian model of nonstationarity in normal and lognormal

processes with applications in Cost-Volume-Profit analysis and life

testing models. A survey of the most relevant literature is provided

in the chapter and will serve to set the stage for the new developments

in the remainder of the thesis.

In this survey, three areas are covered. In Section 2.1 we pre-

sent an overview of probabilistic Cost-Volume-Profit (CVP) analysis and

discuss the most important articles that deal with CVP under conditions

of uncertainty. In Section 2.2 we discuss life testing models with an

emphasis on the exponential, gamma, Weibull and lognormal models. The

review of the literature includes a special section on Bayesian techniques

used in life testing. Finally in Section 2.3 a survey is presented of

some important articles about modeling nonstationary processes.

2.1 Cost-Volume-Profit (CVP), Analysis

Management requires realistic and accurate information to

aid in decision making. Cost-Volume-Profit (CVP) analysis is a widely

accepted generator of information useful in decision making proces-

ses. CVP analysis essentially consists in examining tle relationship

between changes in volume ( output ) and changes in profit. The funda-

mental assumption in all types of CVP decisions is that the firm, or

a department or otl.ier Lype of costing unit, possesses a fixed set

of resources that commits the firm to a certain level of fixed costs

for at least a shortrun period. The decision problem facing a manager

is to determine the most efficient and productive use of this fixed

set of resources relative to output levels and output mixes. The scope

of CVP analysis ranges from determination of the optimal output level

for a single-product department to the determination of optimal output

mix of a large multi-product firm. All these decisions rely on simple

relationships between changes in revenues and costs and changes in

output levels or mixes. All CVP analyses are characterized by their

emphasis on cost and revenue behavior over various ranges of output

levels and mixes.

The determination of the selling price of a product is a

complex matter that is often affected by forces partially or entirely

beyond the control of management. Nevertheless, management must formu-

late pricing policies within the bounds permitted by the market place.

Accounting can play an important role in the development of policy

by supplying management with special reports on the relative profit-

ability of its various products, the probable effects of contemplated

changes in selling price and other CVP relationships.

The unit cost of producing a commodity is affected by such

factors as the inherent nature of the product, the efficiency of oper-

ations, and the volume of production. An increase in the quantity

produced is ordinarily accompaniedd by a decrease in unit cost, pro-

vided the volume attained remains within the limits of plant capacity.

Quantitative data relating to the effect on income of changes in

unit selling price, sales volume, production volume, production costs.

and operating expenses help management to improve the relationships

among these variables. If a change in selling price appears to be de-

sirable or, because of competitive pressure, unavoidable, the possible

effect of the change on sales volume and product cost needs to be

considered.

A mathematical expression of the profit equation of CVP

analysis is:

(2.1.1) Z = Q (P-V) F,

where Z = total profits,

Q = sales volume in units,

P = unit selling price,

V = unit variable cost,

and F = total fixed costs.

This accounting model of analysis has been traditionally

used by the management accountant in profit planning. This use, how-

wver, typically ignores the uncertainty associated with the firm's oper-

ation, thus severely limiting its applicability. During the past 12

years, accountants have attempted to resolve this problem by intro-

ducing stochastic aspects into the analysis.

The applicability of probabilistic models for this analysis

has been claimed because of the realism of such models, i.e., deci-

sions are always accompanied by uncertainty. Thus, the ideal model

is one that gives a probability distribution of the criterion variable,

profit, and that fully recognizes the uncertainty faced by the firm.

The realism of such a model is dependent on logical assumptions for

the input variables and rigorous methodology in obtaining the output

distribution. Further, we hope that,the model can accommodate a wide

range of uses. For example, the capability to handle dependence among

input variables adds a highly useful dimension.

Jaedicke and Robichek (1964) first introduced risk into the

model. They assumed the following relation among the means

(2.1.2) R(Z) = E(Q) [E(P) E(V)] E(F),

where E(.) denotes mathematical expectation.

In addition they assumed that the key variables were all normally

distributed and that the resulting profit is also normally distributed.

Thus, by computing the mean value and standard deviation of the re-

sulting profit function, various probabilistic measures of profit

can be obtained. This model has been depicted as a limit analysis,

since the assumptions of the independent model parameters and the

normalcy of the resulting profit function are not true except in

limiting cases. According to Ferrara, Hayya and Nachman (1972), the

product of two normally and independently distributed variables will

approximate normality if the sum of the two coefficients of variation

is less than or equal to .12.

Others have confronted the same problem of how to identify

the resulting profit distribution when it is not close to a normal

distribution. They have noted that it is often difficult to obtain

analytical expressions for Lhe product of random variables. Because

Lhe appropriate d i :tr inbi ti nal forms for the product of the variable

functions may not be known, Buzby (1974) suggests the application of

Tchebycheff's theorem to stochastic Cost-Volume-Profit analysis. This

theorem, however, permits the analyst to derive only some very crude

bounds on the probabilities of interest, so its value as a decision-

making tool is limited. Liao (1975) illustrated how model sampling

(also called distribution sampling) coupled with a curve-fitting

technique can be used to overcome the above problems associated

with stochastic CVP analysis. In his paper, the illustration of the

proposed approach to stochastic CVP analysis is first developed through

a consideration to the Jaedicke-Robicheck problem, wherein the model

parameters are independent and normally distributed. After that, the

illustration problem is modified to accommodate dependent and non-normal

variates in the problem.

Hilliard and Leitch (1975) developed a model for CVP analysis

assuming a more tractable distribution for the inputs of the equation.

It allows for dependent relationships and permits a rigorous deriva-

tion of the distribution of profit. The problems of assuming price and

quantity to be independent are pointed out. The authors also pointed

out that assuming sales to be normally distributed implies a positive

probability of negative sales.

Probabilities and tolerance intervals for the Hilliard and

Leitch model are obtained from tables of the normal distribution.

The only assumpt ions required for the model are (1) quantity and

contribution margin are lognormally distributed random variables

and (2) fixed cosLs art deterministic. The assumption that sales

quantity and contribution margin are bivariate lognormally distributed

eliminates the possibility of negative sales and of selling prices

below variable costs, and it has the nice additional property that the

product of two bivariate lognormal random variables is also lognormal.

Thus, we can allow for uncertainty in price and quantity and still

have a closed form expression for the probability distribution of

gross profits. H!illi:ird and Leitch can not assume that price and

variable costs are marginally lognormally distributed and have contri-

bution margin also be lognormally distributed. Similarly, if fixed

costs are assumed to be lognormally distributed too, net profits will

not be lognormally distributed.

Adar, Barnea and Lev (1977) presented a model for CVP

analysis under uncertainty that combines the probability characteristics

of the environment variables with the risk preferences of decision

makers. The approach is based on recently suggested economic models

of the firm's optimal output decision under uncertainty, which were

modified within the mean-standard deviation framework to provide for

a cost-volume-utility analysis allowing management to: (1) determine

optimal output, (2) consider the desirability of alternative plans

involving changes in fixed and variable costs, expected price and

uncertainty of price and technology changes and (3) determine the

economic consequences of fixed cost variances.

Dickinson (1974) addresses the problem of CVP analysis under

uncert.i nty by examining l he reliability of using the usual methods

of estimating the means ;nd variances of the past distributions of

sales demand lie emphasized that, when the expectation and variance

of profits are estimated from past data, it is important to differen-

tiate between what, in fact, are estimated and what are true values

of the parameters. In other words, he pointed out that the estimated

expectation of profits, E(T), reflects estimation risk and is not

equal to E(,-). Classical confidence intervals were used for the

expected value of profits, E(n), for the variance of profits, Var(nf),

and for probabilities of various profit levels. However, Dickinson

misinterpreted the classical confidence intervals that he obtains in

his paper. When a classicist constructs a 90 percent confidence interval

for for example, he would state that in the long run, 90 percent

of all such intervals will contain the true value of p. The classical

statement is based on long-run frequency considerations. The classicist

is absolutely opposed to the interpretation that the 90 percent refers

to the probability that the true universe mean lies within the specified

interval. in the eyes of a classicist, a unique true value exists for

the universe mean, and therefore the value of the universe mean can-

not be treated as a random variable. Dickinson's paper also illus-

trates the difficulty of obtaining the probability statements of

greatest interest to management in a classical approach. lis analysis

is only able to provide confidence intervals of probabilities of

profit levels rather than the profit level probabilities themselves.

The problem of parameter uncertainty has been neglected by

the people that have studied CVP analysis under uncertainty. In the

Bayesian approach, 1ine11rtainty regarding the parameters of probability

models is reflected in prior and posterior probability statements

regarding the parameters. Marginal distributions of variables which

depend on those parameters may be obtained by integrating out the

distribution of the parameters, thereby obtaining predictive distri-

butions [see Roberts (1965) and Zellner (1971)] of the quantities of

interest to the manager. These predictive distributions permit one

to make valid probability statements regarding the important quan-

tities, such as profits.

Nonstationarity is another important aspect related to CVP

analysis that no one has considered. In a world that is continually

changing, it is important to recognize that the parameters that

describe a process at a particular point in time may not do so at

a later point in time. In the case of the variable sales, for instance,

experience shows that it is typically affected by a variety of eco-

nomic and political events. Thus, a CVP model ideally should include

the changing character of the process by allowing for changes in the

parametric description of tle process through time. Failure to recog-

nize the nonstationary conditions may result in misleading inferences.

In this dissertation the problem of- Cost-Volume-Profit analy-

sis will be considered from a Bayesian viewpoint, and inferences under

a special case of nonstationarity will be considered. Also the Bayesian

results under nonstationarity will be compared with those results

that can be obtained under a stationary Bayesian model, and the Baye-

sian model will hib compared with some alternative approaches.

2.2 Life Testing Models

2.2.1 Introduction

The development of recent technology has given special impor-

tance to several problems concerning the improvement of the effective-

ness of devices of various kinds. It is often important to impose

extraordinarily high standards on the performance of these devices,

since a failure in the performance could bring disastrous consequences.

The quality of production plays an important role in today's life. An

interruption in the operation of a regulating device can lead not

only to deterioration in the quality of a manufactured product but

also to damage of the industrial process. From a purely economic view-

point high reliability is desirable to reduce costs. However, since

it is costly to achieve high reliability, there is a tradeoff. The

failure of a part or component results not only in the loss of the

failed item hut often results in the loss (at least temporarily) of

some larger assembly or system of which it is part. There are nu-

merous examples in which failures of components have caused losses

of millions of dollars and personal losses. The space program is an

excellent example where even the lives of some astronauts were lost

due to failure in the system. The following authors have considered

the statistical theory of reliability and provide a good set of re-

ferences on the subject: Mendenhall (L958), Buckland (1960), Birnbaum

(1962), Govindarajlu l (1 )4), Mann, Schaefer and Singpurwalla (1973),

and Canfield and Borgman (1975).

Re ability tchuury is lih disciple ine that deals wi th procedures

to ensure the maximum effectiveness of manufactured articles

and that develops methods of evaluating the quality of systems from

known qualities of their component parts. A large number of problems

in reliability theory have a mathematical character and require the

use of mathematical tools and the development of new ones for their

solution. Areas like probability theory and mathematical statistics

are necessary to solve some of the problems found in reliability

theory. No matter how hard the company works to maintain constant

conditions during a production process, fluctuations in the production

factors lead to a significant variation in the properties of the

finished products. In addition, articles are subjected to different

conditions in the course of their use. To maintain and to increase

the reliability of a system or of an article requires both material

expenditures and scientific research.

Statist c.ia theory and methodology have played an influen-

tial role in the development of reliability theory since the publi-

cation of the paper by Epstein and Sobel (1953). Four statistical

concepts provide the basis for estimating relevant parameters and

testing hypotheses about the life characteristic of the subject

matter. These concepts are:

(i) the distribution function of some variable which is a

direct or indirect measure of the response (life time) to usage in

a particular euvirunment;

(ii) the associated probability density (or frequency)

fiinC ti on

(iii) the survival probability function; and

(iv) the conditional failure rate.

A failure distribution provides a mathematical description

of the length of life of a device, structure or material. Consider

a piece of equipment which has been in a given environment,e. The

fatigue life of this piece of equipment is defined to be the length

of time, T(e), this piece of equipment operates before it fails. Full

information about e would fully determine T(e), so that given e, T(e)

would not be random. One source of randomness in life is in uncertainty

about the environment, i.e., T(e) is a random variable because e is

random. Equipment has different survival characteristics depending on

the conditions under which it is operated, and e provides a statement

of what conditions are but does not determine T(e) Fully.

The reliability of an operating system is defined as the

probability that the system will perform satisfactorily within

specified conditions over a given future time period when the system

starts operating at some time origin. Different distributions can

be distinguished according to their failure rate function, which

is known in the literature of reliability as a hazard rate [see

Barlow and Prosch:n (1965)]. The hazard rate (denoted by h), which

is a function of time, gives the conditional density of failure at

time, t,with the hypothesis that the unit has been funcitoning with-

out failure up to that point in time. The conditional failure is

defined as:

!(t) = f(t)/[l F(t)] = f(t)/R(t) ,

(2.2.1)

where (2.2.2) F(t) = Prob (T < t) = ft f(t) ds,
-CO

is the probability that an observed value of T will be less than or

equal to an assigned number t. The reliability function (also called

the survival function) of the random variable T gives the probability

that T will exceed t and is defined by

(2.2.3) R(t) = 1 F(t) = Prob (T > t).

The probability density function of the random variable T, f(t),

0 < t < oo, is known as the failure density function of the device.

It can be shown that the conditional failure rate and the distribu-

tion function of a random variable are related by

(2.2.4) F(t) = 1 exp[- ft h(s) d(s)].
0

The causes of failure can be categorized into three basic

types. It is recognized, however, that there may be more than one

contributing cause to a particular failure and that, in some cases,

there may be no completely clearcut distinction between some of the

causes. The three classes of failure are infant mortalities, or

early failures, random failures and wearout failures. The behavior

of the hazard rate as a funciton of time is sometimes known as the

hazard function or life characteristic of the system. For a typical

system that may experience any of the three previously described types

of failure, the life characteristic will appear as in Figure 1. The

representation of the life characteristic has been classically referred

to as the "bathtub curve", wherein the three segments of the curve

represent the three time periods of initial, chance and wearout failure.

Hazard

rate

Initial Random failure Wearout
Random failure
failure failure

Time
Figure 1. Life characteristics of some systems

The initial failure period is characterized by a high hazard rate

shortly after time x=O and a gradual reduction during the initial

period of operation. During the chance failure period, the hazard

rate is constant and generally lower than during the initial period.

The cause of this failure is attributed to unusual and unpredictable

environmental conditions occurring during the operating time of the

system or of the device. The hazard rate increases during the wearout

period. This failure is associated with the gradual depletion of a

material or an accumulation of shocks and so on.

In the following subsections we will consider the general

properties of some widely used life distributions, the assessment

and use of those distributions, and the literature related to Bayesian

methods in life testing.

2.2.2 Some Common Life Distributions

..2.2.1 The Exponential Distribution

In the case of a constant failure rate the distribution of

life is exponential. This case has received the most emphasis in the

literature, since, in spite of theoretical limitations, it presents

attractive statistical properties and is highly tractable. Data

arising from life tests under laboratory or service conditions are

often found to conform to the exponential distribution.

An acceptable justification for the assumption of an expo-

nential distribution to life studies was initially presented by Davis

(1952). More recently Barlow and Proschan (1965) have advanced a mathe-

matical argument to support the plausability of the exponential dis-

tribution as the failure law of complex equipment. The random variable

T has an exponential distribution if it has a probability density

function of the form

(2.2.5) fT(t) = o-" exp[-(t-O)/o] t > 6,

o > 0.

The mean and variance ot T are (0 + 0) and o2, respectively. In most

applications 0 is taken as zero. For this distribution, the physical

interpretation of a constant hazard function is that, irrespective of

the time elapsed since the start of operation,of a system the prob-

ability th:it the system fail in the next time intervals dt,

given that it has survived to time t, is independent of the elapsed

time t and is constant.

2.2.2.2 The Gamma Distribution

An extremely useful distribution in fatigue and wearout

studies is the gamma distribution. It also has a very important rela-

tionship to the exponential distribution, namely, that the sum of n

independent and identically distributed (i.i.d.) exponential random

variables with common parameters e=0 and o is a random variable that

has a gamma distribution with parameters n and a. Hence, the exponen-

tial distribution is a special case of the gamma with n=l.

The random variable T has a gamma distribution if its pro-

bability density function is of the form,

(2.2.6) f (t) = {(t- )n-1 exp[-(t-0)/o]} /onr(n); n > 0,
o > 0,
o > 0.

The standard form of the distribution is obtained by putting o=l and

0=0, giving

(2.2.7) f (t) = [tn- exp(-t)]/ (n), t > 0;

where the gamma function, denoted F, is a mapping of the interval

(0,) into itself and is defined by

(2.2.8) F(n) = tn-1 exp(-t) dt.
0
The probability distribution function of (2.2.7) is

(2.2.9) 'roblT < t] = lr(n)]-1 ft xn-1 exp(-x) dx
0

Since a distribution of the form given in equation (2.2.6)

can he obtained from standardized distributions, as in equation (2.2.7),

by the linear transformation t=(t'-6)/o. there is no difficulty in

deriving formulas for moments, generating functions,etc., for equation

(2.2.6) from those for equation (2.2.7).

One of the most important properties of the distribution is

the reproductive property; if T1 and T2 are independent random variables

each having a distribution of the form (2.2.7), possibly with different

values n', n" of n but with common values of o and 0, then (T1+ T2)

also has a distribution of this form, with the same value of o and

0, and with n = n' + n".

2.2.2.3 The Weibull Distribution

The Weibull distribution was developed by W. Weibull (1951)

of Sweden and used for problems involving fatigue lives of materials.

Three parameters are required to uniquely define a particular Weibull

distribution. Those three parameters are the scale parameter o, the

shape parameter n and the location parameter 0.

A random variable T has a Weibull distribution if there are

values of the parameters n (>0), o (>0) and 0 such that,

(2.2.10) Y = [(t-e)/on

has the exponential distribution with probability density function

(2.2.11) ty(y) = exp(-y), y > 0.

The probability density function of T is given by

(2.2.12) fT(t) = no- [(t-0)/o]n- exp (t- )/o]} t > .

The standard Weibuill distribution is obtained by putting o=1 and

0=0. The value zero for 0 is by far the most frequently used, espe-

cially in representing distributions of life times.

The Weibull distribution has cumulative distribution function

(2.2.13) FT(t) = ]-exp{-[(t-0)/o]n ,

and its mean and variance are

(2.2.14) E(t) = oF(l + [1/n])

and (2.2.15) Var(t) = o2{F(l+[2/n]) F2(l+[l/n])} respectively.

For the two parameter Weibull distribution we have that the reliability

and hazard function are

(2.2.16) R (L) = exp [-(t/o)n]

and
(2.2.17) h (t) = nt-1/n

When n=l, the hazard function is a constant. Thus the exponential dis-

tribution is a special case of the Weibull distribution with n=l.

2.2.2.4 The Lognormal Distribution

The lognormal distribution is also a very popular distribution

in describing wearoit failures. This model was developed as a physical

or, more appropriately biological, model associated with the theory

of proportion Le effects (see Aitchison and Brown (1957) for a full

description of thle distribution, its properties, and its developments).

Briefly, if a random variable is supposed to represent the magnitudes

at succesive points of time of, for example, a fatigue crack or the

growth of biological organisms and the change between any pairs of

successive steps or stages is a random proportion of the previous

size, then asymptotically the distribution of the random variable is

lognormal [see Kapteyn (1903)]. This theoretical result imparted some

plausibility to the lognormal distribution for failure problems. Let

tl< t2< ... < t be a sequence of random variables that denote the

sizes of a fatigue crack at succesive stages of its growth. It is

assumed that the crack growth at stage i, t.- t.i1, is randomly

proportional to tle size of the crack, t.1 and that the item fails

when the crack reaches t Let ti- t = t_.L i= 1, 2, ..., n, where

7. is a random variable. The ni are assumed to be independently dis-

tributed random variables that need not have a common distribution

for all i's when n is large but that need to be lognormally distrib-

uted otherwise. Thus,

1ri = (t t i = 1 2, . n .

Mann, Schaefer and Singpurwalla (1973) show that In tn, the life

length of the item, for large n, is asymptotically normally distri-

buted, and hence tn has a lognormal distribution.

If there is a number y such that

(2.2.18) Z = In(t-y)

is normally disti-ibilted, then the distribution of t is said to be

lognormal. The distribution of t can be defined by the equation,

(2.2. 19) U = , + 6 ln(tL- ) ,

where U is a unit normal variable and 0, 6 and y are parameters. The

probability density function of T is defined by

(2.2.20) fT(t) = 6[(t-y)/Ji ]- exp[-{f+61n(t-y)}2/2], t>y.

An alternative, more fashionable notation replaces 0 and 6 by the

expected value p and standard deviation o of Z = In(t-y). The two

sets ot parameters are related by the equations,

(2.2.21) p = -0/6

and

(2.2.22) o = 6-

so that the distribution of t can be defined by

(2.2.23) U = [In(t-y) p]/o

and the probabiliLy density function of T by

(2.2.24) fT(t) = [(t-y)/2Ro]-1 exp[-{ln(t-y)-p}2/2U2], t>y.

In many applications, y is known (or assumed) to be zero.

This important case has been given the name two parameter lognormal

distribution. The mean and variance of the two parameter distribution

are given by

(2.2.25) E(t) = exp[P + (2/2)] ,

and

(2.2.26) Var(t) = [exp(211) ] ((a-l) ,

where M = rxp(O2).

In addition, the value t such that Pr(t
corresponding percentile, U of the unit normal distribution by the

relation,

(2.2.27) t = exp(p + U a).

Applications of the lognormal distribution have appeared in

many diverse areas, e.g., environmental health [see Dixon (1937) and

Hill (1963)], air pollution control [see Singpurwalla (1971, 1972),

Larsen (1969) and others like economics and insurance claims [see

Wilson and Worcester (1945)] application of the distribution is not

only based on empirical observation, but in some cases is supported

by theoretical arguments.

For example, such arguments have been made in the distribution

of particle sizes in natural aggregates and in the closely related

distribution of dust concentration in industrial atmospheres [see

Tomlinson (1957) and Oldham (1965)]. The lognormal distribution has

also been found to be a serious competitor to the Weibull distribution

in representing life time distributions for manufactured products.

Among our references, Adams (1962), Ansley (1967), Epstein (1947,

1948), Farewell and Prentice (1977), Govindarajulu (1977), Goldthwaite

(1961), Gupta (1962), Hald (1952) and Nowick and Berry (1961) refer

to this topic. Other applications in quality control are described

by Ferrell (1958), Morrison (1958) and Rohn (1959). Many of these

applications are also referenced by Aitchison and Brown (1957),

Finney (1941) and Gupta et al. (1974).

2.2.3 Traditional Approach to Life Testing Inferences

In life testing theory we find a large number of random quan-

tities. In most cases we do not know the distributions and theoretical

characteristics; our aim is to estimate some of these quantities. This

is usually accomplished with the aid of observations on the random

variables. According to the laws of large numbers, an "exact" deter-

mination of a probability, an expected value, etc., would require an

"infinite" number of observations. Having samples of finite size,

we can do no more than estimate the theoretical values in question.

The sample characteristics, or statistics, serve the purpose of sta-

tistical estimation. For a good estimation of theoretical quantities,

a fairly large sample is sometimes needed. In many practical situations

the following two types of estimation problems arise. A certain quan-

tity, say 6, which is, from the statistical point of view, a theo-

retical quantity, has to be determined by means of measurement. Such a

quantity may be, for example, the electrical resistance of a given

device, the life of a given product, etc. The result T of the mea-

suring procedure is a random variable whose distribution depends on

o and perhaps on additional quantities. That is,we have to estimate

the parameter 0 out of a sample T1, T, ... T taken on T. In the
n

other case, tile quantity in question is a random variable itself

and in such cases we are interested in the (theoretical) average

value, or tie dispersion of T, etc. This means that we have to es-

timate the expected value E(T) or Var(T), and perhaps other (constant)

quantities that can be expressed with the aid of the distributed on

function of T, like the reliability function. More often for lifetime

distributions, the quantity of interest is a distribution percentile,

also known as the reliable life of the item to be tested, corresponding

to some specified population survival proportion; or it is the pop-

ulation proportion surviving at least a specified time, say S

For the classical statistician,the unknown parameter 6 is

considered to be a constant. In estimating a constant value there

are various aspects to consider. If we wish to have an estimator

whose value can be used instead of the unknown parameter in formulas

[certainty equivalent (CE) approach], then the estimator should

have one given value. In this case we speak of point estimation. But

knowing that our estimator is subject to error, sometimes we would

like to have some information on the average deviation from the

value. In this case we have to construct an interval that contains

the unknown parameter, at least with high probability, or give a

measure of the variability of the estimator (such as the standard

approach to life testing inferences is focused in two areas; one

relates to point and interval estimation procedures for lifetime

distributions and the other relates to methods of testing statisti-

cal hypotheses in reliability (known as "reliability demonstration

tests").

The classical approach to point estimation in life testing

inferences emphasizes that a good estimator should have properties

like unbiasedness, eff iciency, consistency and sufficiency [see

Dubey (1968), Bartlett (1937) and Weiss (1961). Two methods, the

method of moments and method of maximum likelihood, are frequently

used to yield estimators with as many as possible of the previously

mentioned properties. Under various sampling assumptions, the maxi-

mum likelihood estimators of the parameters were obtained for the

following distributions; gamma Isee Choi and Wette (1969) and Harter

and Moore (1965) ; Weibull [see Bain (1972), Billman et al. (1971),

Cohen (1965), Englehardt (1975), Haan and Beer (1967), Lemon (1975)

and Rockette et aL. (1973)1; exponential [see Deemer and Votaw (1955),

El-Sayyad (1967) and Epstein (1957)]; and for the normal and lognormal

[see Cohen (1951), Hlarter and Moore (1966), Lambert (1964) and Tallis

and Young (1962)]. The traditional approach also includes some linear

estimation properties like Best Linear Unbiased (BLU) and Best Linear

Invariance (BLI).

Interval estimation procedures have also been developed for

the parameters of the life distributions. Examples include Bain and

Englehardt (1973), Epstein (1961), carter (1964) and Mann (1968).

Point or interval estimators for functions of the life distributions,

such as reliable life, reliability function, hazard rate, etc. were

obtained by substituting for the unknown parameters the point or inter-

val estimators obtained for them [see Johns and Lieberman (1966),

Bartholomew (1963), (rubbs (1971), Harris and Singpurwalla (1968, 1969),

Lawless (1971,1972), Iikes (1967), Mann (1969-a, 1969-b, 1970), Varde

(1969) and Linhart ( )5) ].

Testing reliability hypotheses is the second major area of

research in the classical approach to life testing. By means of

the methods referenced previously, a test statistic is selected,

regions of acceptance and rejection are set up, and risks of in-

correct decisions are calculated. In addition it is emphasized

that the risks of incorrect decisions are specified before the

sample is obtained, and in this case n, the sample size, is gene-

rally to be determined. Some of the references in this area include

[Epstein (1960), Epstein and Sobel (1955), Kumar and Pate] (1971),

Lilliefors (1967, 1969), Sobel and Tischendorf (1959), Thoman et al.

(1969, 1970) and Fercho and Ringer (1972)].

A large part of the statistical problem in reliability in-

volves the estimation of parameters in failure models. Each of the

methods of obtaining point estimates previously referenced has

certain statistical properties that make it desirable, at least

from a theoretical viewpoint. Not surprisingly, point estimates

are often made (particularly in reliability) because decisions are

to be based on them. The consequences of the decisions based on the

estimates often involve money, or, more generally, some form of

utility. Hence the decision maker is more interested in the practi-

cal consequence of the estimate than in its theoretical properties.

In particular, he may be interested in making estimates that mini-

mize the expected loss (cost), but this can not be accomplished in

general with classical methodology because the methodology does not

admit probability distributions of the parameters.

92. 2. Bayesian Techniques in Life Testing

The non-Bayesian (classical) approach to estimation con-

siders an unknown parameter as fixed. This means that classical in-

terval estimation and hypothesis testing must lean on inductive

reasoning either through the likelihood function or the sampling

distributions. In point estimation, the classical approach must

depend on estimates the criteria for which often are not based on

the practical consequences of the estimates. On the other hand,Bayes

procedures assume a prior distribution of the parameter space, that

is,considers the parameter as a random variable, and,hence, the pos-

terior distribution is available. This creates the possibility of

a whole new class of criteria for estimation, namely, minimization

of expected loss, probability intervals and others.

In view of the difficulty in assessing utility or costs of

complex reliability problems, in previous studies Bayesian methods

have been used primarily to provide a means of combining previous

data (expressed as the prior distribution) with observed data

(expressed in the likelihood function) to obtain estimates of parame-

ters by using the posterior density. However, it must be emphasized that

Bayesian methods are perfectly general in providing whatever the

reliability problem demands.

Tlhre is a loss function that is rather popular in Bayesian

analysis and gives simple results. Suppose that 6 is an estimate of

)and that the loss function is

(-'.2. 28) 1(k,) = (0 O) 2.

This function states that the loss is equal to the square of the

distance of ; from 0. The Bayes approach is to select the estimate

of 6 that minimizes the expected loss with respect to the posterior

distribution. The estimate that accomplishes this is the posterior

mean, that is,

(2.2.29) 6 = E(61tl, t2, ... tn;P)

where P represents prior information. The above loss function is often

called the quadratic loss function and the posterior mean is termed

the Bayes estimate. If the loss function is of the form

(2.2.30) L(6,6) = 6-6 ,

the estimate of 0 that minimizes the expected loss is the median of

the posterior dLstribution. Canfield (1970) developed a Bayesian es-

timate of reliability for the exponential case using this loss function.

The resulting estimate is seen to be the MVUE of reliability when the

prior is flat. A third and simple case is the asymmetric linear,

k (6-6) if 0>6
(2.2.31) L(6,6) = (- if 6
k (6-6) if 6<6.

The estimate of 6 that minimizes the expected loss if the ku/(k0+ k )

fractile, [see Raiffa and Schlaifer (1961)]. Beyond these three

simple cases, things become difficult in regard to loss function for

two reasons:

(i) difficulties in assessing a realistic loss function

and

(ii) mathematical intractability.

The expected loss is generally a random variable a prior since it

depends on the as yet unobserved sample data. The unconditional ex-

pectation (with respect to the sample) of the expected loss is called

the "Bayes risk" and is minimized by the Bayes estimate.

Bayes methods have been used in a variety of areas of re-

liability. Most uses can be characterized as point or interval esti-

mation of parameters of Life distributions or of reliability functions.

Examples include Breipohl, et. al., (1965) who studied the be-

havior of a family of Bayesian posterior distributions. In addition

the properties of the mean of the posterior distribution as a point

estimate and a method for constructing confidence intervals were

given. The problem of hypothesis testing was considered, among others,

by MacFarland (1972). He provided a simple exposition of the rudi-

ments of applying Bayes equation to hypotheses concerning relia-

bility.

The Bayesian approach has also been applied to parameter

estimation and reliability estimation of some known distributions

like gamma, Poisson, lognormal and others. Lwin and Singh (1974)

considered a Bayesian analysis of a two-parameter gamma model in

life testing context with special emphasis on estimation of the

reliability function. The Poisson distribution has received the

attention of Canavos (1972, 1973). In the first article a smooth

empirical Bayes estimator is derived for the hazard rate. The re-

liability function is also estimated either by using the empirical

Bayes estimate of the parameters, or by obtaining the expectation

of the reliability function. Results indicate a significant reduc-

tion in mean squared error of the empirical Bayes estimates over

the maximum likelihood estimates. A similar result was also derived

for the exponential distribution by Lemon (1972) and by Martz (1975).

Next, Canavos developed Bayesian procedures for life testing with

respect to a random intensity parameter. Bayes estimators were

derived for the Poisson parameters and reliability function based

on uniform and gamma prior distributions. Again, as expected, the

Bayes estimators have mean squared errors (MSE) that are appreciably

smaller than those of the minimum variance unbiased estimator (MVUE)

and of the maximum likelihood estimator (MLE).

Zellner (1971) has studied the Bayesian estimation of the

parameters of the lognormal distribution. Employing a flat prior,

Zellner found that the NSE estimators of the parameters are the

optimal Bayesian estimators when a relative squared error loss

function is used.

The Weibull and exponential function have received most

of the attention of authors who have studied life distributions

from a Bayesian viewpoint. The Weibull process with unknown scale

parameter is taken as a model by Soland (1968) for Bayesian decision

theory. The family of natural conjugate prior distributions for the

scale parameter is used in prior and posterior analysis. In addition,

preposterior analysis is given for an acceptance sampling problem

with utility linear in the unknown mean of the Weibull process. Soland

(1969) extended the analysis by treating both the shape and scale

parameters as unknown, hut as was previously known i t is not possi-

ble to find a family of continuous joint distributions on the two

parameters that is closed under sampling, so a family of prior dis-

tributions is used that places continuous distributions on the scale

parameter and discrete distributions on the shape parameter. Prior

and posterior analysis are examined and seen to be no more difficult

than for the case in which only the scale parameter is treated as

unknown, but posterior analysis and determination of optimal sampling

plans are considerably more complicated in this case.

In Bury (1972), a two-parameter Weibull distribution is

assumed to be an appropriate statistical life model. A Bayesian decision

model is constructed around a conjugate probability density function

for the Weibull hazard rate. Since a single sufficient statistic of

fixed dimensionality does not exist for the Weibull model, Bury was

able to consider only two sampling plans in his preposterior analysis:

obtain one further observation or terminate testing. Bury points out

that small sample Bayesian analysis tends to be more accurate than

classical analysis because of the additional prior information utilized

,in the analysis. Bayes credible bounds for the scale parameter and

for the reliability function are derived by Papadopoulos and Tsokos

(1975).

Reliability data often include information that the failure

event has not yet ccuIrred for some items, while observations of

complete lifetimes are available for other items. Cozzolino (1974)

addressed this problem from a Bayesian point of view, considering

density functions that have failure rate functions consisting of a known

function multiplied by an unknown scale factor. It is shown that a gamma

family of priors is conjugate for the unknown scale parameter for both

complete and incomplete experiments. A very flexible and convenient

model resulting from the assumption of a piecewise constant failure function.

Life tests that are terminated at preassigned time points or

after a preassigned number of failures are sometimes found in reliabil-

ity theory. Bhattaclarya (1967) provided a Bayesian analysis of the

exponential model based on this kind of life test. He showed that the

reliability estimate for a diffuse prior (which is uniform over the

entire positive line) closely resembles the classical MVUE, and he

considered the role of prior quasi-densitiesL when a life tester has

no prior information. Bhattacharya points out that the use of constant

density over the positive real line has been suggested to express

ignorance but that it causes problems. For example it can not be

interpreted as a probability density since it assigns infinite measure

on the parameter space. [See Box and Tiao (1972).]

A paper by Dunsmore (1974), stands out from among the other

Bayesian papers in life testing and is particularly pertinent to

important exception because it carries the Bayesian approach to its

natural conclusions by determining prediction intervals for future

If g(O) is any non-negative function defined in the parame-
ter space S& such that g(O) 0 V 0 c Q, then g(b) is called a prior
quasi-densi ty.

observations in life testing using the concept of the Bayesian pre-

dictive distribution. One objective of prediction is to provide some

estimate either point or interval, for future observations of an

experiment F based on the results obtained from an informative experi-

ment E. As we mentioned before, the classical approach to prediction

involves the use of tolerance regions. [See Aitchison (1966), Folks

and Browne (1975), Guenther et al. (1976) and Hewett and Moeschberger

(1976)]. In these we obtain a prediction interval only, and the

measure of confidence refers to the repetitions of the whole experimental

situation. The Bayesian approach on the other hand, allows us to

incorporate further information which might be available through a

prior distribution and leads to a more natural interpretation.

Let t ..., t he a random sample from a distribution with

probability density function P(t 6), (tcT;0cO), and let yl', y2 .... yn

be a second independent random sample of "future" observations from

a distribution with probability density function P(ylo), (ycY;OcO).

Our aim is to make predictions about some function of y y2' "..' Y 1

The Bayesian approach assumes that a prior density function P(0),

(Oce) is available that measures our uncertainty about the value of 6.

If the information in E is summarized by a sufficient statistic t

then a posterior distribution P(eOt) is available. Suppose now that

we wish to predict some statistic y defined on y y2 .' yn. Then

Such a suffice ie nt statistic will always exist since, for
T
example, t co Ild I tl ie vector (tI, L, ... t )
1" n

the predictive density function for y is given by

(2.2.32)

P(y t) = f P(y|e) P(olt) de

A Bayesian prediction interval of cover is then defined as an in-

terval I such that,

(2.2.33) P(l t) = f P(ylt) dy = 8.

[See, for example, Aitchison and Sculthorpe (1965), Aitchison (1966)

and Guttman (1970).] It should be emphasized that in the Bayesian

approach the complete inferential statement about y is given by the

predictive density function P(y t). Any prediction interval is only

a summary of the full description P(y t).

In general there will be many intervals I that satisfy (2.2.33).

Dunsmore considers most plausible Bayesian prediction intervals

(commonly known as highest posterior density (HPD) intervals) of cover

B, which have the form,

(2.2.34) I = [y:P(y It) > ],

where A is.determined by P(I t) = B.

In conclusion we might say that the-uses of Bayesian methods

in life testing have been limited. However in those cases where Bayes

estimators have been found, they performed better, according to clas-

sical criteria, than the conventional ones. The use of loss functions

has not been analyzed deeply for the reasons mentioned before; namely

1
It is implicitly assumed in (2.2.32) that conditional on
6, y and t are independent.

that the loss function is usually complex and unknown, and that even

when the loss function is known the Bayes estimate is sometimes dif-

ficult to find. Some of these problems wil.L be solved with the develop-

ment of mathematical theory and probably with the development of

computer systems. Only the Dunsmore paper fully used the Bayesian method-

ology to obtain prediction intervals that consider all available in-

formation and fully recognize the remaining parameter uncertainty.

All of the papers discussed in the previous section con-

sidered a stationary situation. That is, the known parameters and

the distributions used are assumed to remain the same across all

time periods. It would be of value to study the nonstationary case,

where the parameters are changing in time and possibly the distri-

butions could also change in time. It is important to recognize,

however, that probably the problems now faced with the stationarity

assumption will be greater when that assumption is relaxed. Never

this dynamic system is well worth investigating.

2.3 'Modeling of Nonstationary Processes

For many real world data generating processes the assump-

tion of stationarity is questionable. Take for instance life testing

models. When it is assumed that the life of certain commodities

follows a lognormal distribution, for example, the stationarity as-

sumption could beC expected to hold over short periods of time; but

in most cases it would ihe expected that for a lengthy period, sta-

Lionarity would bh, a doubtful assumption. If the model represents

the life of perishable products, ike food for example, then it

would be expected that environmental factors like heat and humidity

could change and affect the characteristics of the life distribution

of the product or affect the input factors used in the manufacturing

process. Furthermore, the wearout of the machines used in the manu-

facture of the products could cause changes in the quality of the pro-

ducts and hence in the parameters of the life distributions.

Random parameter variation is surely to be a reasonable as-

sumption when we are concerned with economic variables, like those

used in Cost-Volume-Profit analysis. A wide spectrum of circumstances

could be mentioned where the economic environment is gradually

affected. For example, the level of economic development changes

related variables like income, consumption and price. Also, consumer's

tastes and preferences evolve relatively slowly as social and economic

conditions change and as new marketing channels or techniques are

developed. The gradual increase in technology available to the indus-

try and to the government may produce changes that are not dramatic

but tlat will have some influence in any particular period of time.

In other words, it seems reasonable to assume that in at least some

situations the distribution functions of variables, like sales, price

or costs, could be gradually changing in time. It is important to

emphasize that we are referring to gradual changes, the effects of

which are not perfIctly predictable in advance for a particular period.

If a data generating process characterized by some parameter

0 is nonstationary, then it is not particularly realistic to make

inferences and decisions concerning 0 as if 0 only took on a single

value. Instead we should he concerned with a sequence 6 02, ... of

values of 0 corresponding to different time periods, assuming the

characteristics of the process vary across time but are relatively

constant within a given period. Some researchers have studied this

problem with particular stochastic processes.

Chernoff and Zacks (1964) studied what they called a "tracking"

problem. Observations are taken on the successive positions of an

object traveling on a path, and it is desired to estimate its current

position. If the path is smooth, regression estimates seem appropriate.

However, if the path is subjected to occasional changes in direction,

regression will give misleading results. Their objective was to arrive

at a simple formula which implicitly accounts for possible changes in

direction and discounts observations taken before the latest change.

Successive observations were assumed to be taken on n independently

and normally distributed random variables with means p', 2', ..' n"

Each mean is equal to the preceding mean except when an occasional

change takes place. The object is to estimate the current mean p n

They studied the problem from a Bayesian point of view and made the

following assumptions: the time points of change obey an arbitrary

specified a priori probability distribution; the amounts of change

in the means (when changes take place) are independently and normally

distributed random variables with zero mean; and the current mean

n is a normally distributed random variable with zero mean. Using

a quadratic loss function and a uniform prior distribution for p1 on

the whole real line they derived a Bayes estimator of p In ad-

dition they derived the minimum variance linear unbiased (MVLU)

estimator of 1 n. Comparing both estimators they found that although

the MVLU estimator is considerably simpler than the Bayes estimator,

when the expected number of changes in the mean is neither zero nor

n-1 the Bayes estimator is more efficient than the MVLU.

Chernoff and Zacks studied an alternative problem in which the

prior distribution of time points of change is such that there is at

most one change. This problem leads to a relatively simple Bayes esti-

mator. However, difficulties may arise if this estimator is applied

when there are actually two (or more) changes. The suggested technique

starts at the end of a series, searches back for a change in mean and

then estimates the mean value of the series forward from the point at

which such a change is assumed to have occurred. They designed a procedure

to test whether a change in mean has occurred and found a simpler test

than the one used by Page (1954, 1955). Most of the results appearing in

this paper were derived in a previous paper by Barnard (1959) in a some-

what different manner, but the general results are essentially the same.

The previous paper by Chernoff and Zacks motivated some

research in the following years. Mustafi (1968) considered a situa-

tion in which a random variable is observed sequentially over time

and the distribution of this random variable is subjected to a pos-

sible, change at very point in the sequence. The study of this prob-

lem is centered about the model introduced by Chernoff and Zacks.

Three aspects of the problem were considered by Nustafi. First he

considered the problem of estimating the current value of the mean

on the basis of a set of observations taken up to the present. Chernoff

and Zacks assumed Lhat certain parameters occurring in the model were

known. Mustafi then derives a procedure for estimating the current

value of the mean on the basis of a set of observations taken at

successive time points when nothing is known about the other parame-

ters occurring in the model. Second Mustafi estimated the various

points of change in the framework of an empirical Bayes procedure and

used an idea similar to that of Taimiter (1966) to derive a sequence

of tests to be applied at each stage. Third he considers n independent

observations of a random variable that belong to the one parameter

exponential family taken at successive time points. He examines the

problem of testing the equality of these n parameters against the

alternative that the parameter has changed r-times at some unknown

points where r is some finite positive integer less than n. He de-

veloped a test procedure generalizing the techniques used by Kender

and Zacks (1966) and Page (1955).

Hinich and Farley (1966) also studied the problem of estima-

tion models for time series with nonstationary means. They assumed

a model similar to the one developed by Chernoff and Zacks except

that they assumed that the number of points of change per unit time

are Poisson distributed with a known shift rate parameter. They found

an estimator for tie mean which is unbiased and efficient. Also it

turned out to be a linear combination of the vector of observations.

The Farley-llinich technique attempts to estimate jointly the level

of the mean at the beginning of a series as well as the size of the

change (if any).

Farley and Hinich in a later paper (1970) compared the method

developed in (1966) with the one presented by Chernoff and Zacks (1964)

and later generalized by Mustafi (1968). Some ways were examined to

systematically track time series which may contain small stochastic

mean shifts as well as random measurement errors. A "small" shift

is one which is small relative to measurement error. Three approaches

were tested with artificial data, by means of Monte Carlo methods,

using mean shifts which were rather small, that is, mean shifts which

were half the magnitude of random measurement error variance. Several

false starts with actual marketing data showed that there was an iden-

tification problem to provide an adequate test of the procedures'

performance, and artificial data of known configuration provided a

more natural starting point. Two techniques (one developed by the

authors and the other by Chernoff and Zacks) involved formal estimation

under the assumption that there was at most one discrete jump in a

data record of fixed length of the type often stored in an information

system. Both techniques performed reasonably well when the rate of

shift occurrence was known, but both techniques are very sensitive

to prior specification of the rate at which shifts occur in

terms of both classes of errors, that is, missing shifts which

occur and identifying "shifts" which do not occur. Knowing the

shift rate precis ly and knowing that more than one shift in a record

is extremely unlikely are two very severe restrictions for many ap-

plications. A simpler filter technique was tested similarly with more

promising results in terms of avoiding both classes of errors. The

filter approach involved first smoothing the series and then imple-

menting ad hoc decision rules based on consecutive occurrences of

smoothed values falling outside a predetermined range around the

moving average.

Hlarrison and Stevens have produced two important papers about

Bayesian forecasting using nonstationary models. In the first of these

papers (1971), they described a new approach to short-term forecasting

based on Bayesian principles in conjunction with a multi-state data-

generating process. The various states correspond to the occurrence of

transient errors and step changes in trend and slope. The performance

of conventional systems, like the growth models of Holt] (1957), Brown

(1963) and Box-Jenkins (1970), is often upset by the occurrence of

changes in trend ,nd slope or transients. In Harrison and Stevens'

approach events of this nature are modelled explicitly, and succes-

sive data points are used to calculate the posterior probabilities

of such events at each instant of time.

In the second paper (1976), Harrison and Stevens describe a

more general approach to forecasting. The principles of Bayesian fore-

casting are discussed and the formal inclusion of the "forecaster"

in the forecasting system is emphasized as a major feature. The criti-

cal distinction is that between a statistical forecasting method and

a forecasting sysltcm. The former transform input data into output in-

formation in a purely mechanical way. The latter, however, includes

people: the person responsible for the forecast and all the people

concerned with using the forecasts and supplying information relevant

to the resulting actions. It is necessary that people can communicate

their information to the method and that the method clearly communi-

cates the uncertain information in such a way that it is readily

interpreted and accepted by decision makers. The basic model, called

by them "the dynamic linear model", is defined together with Kalman

filter recurrence relations and a number of model formulations are

given based on their result. They first phrase the models in terms

of their "natural" parameters and structure, and then translate them

into the dynamic linear model form. Some of the models discussed by

them are, a) regression models, b) the steady model, c) the linear

growth model, d) the general polynomial models, e) seasonal models,

f) autoregressive models, and g) moving average models.

Multiprocess models introduce uncertainty as to the under-

tying model, itself, and this approach is described in a more general

fashion than in their 1971 paper. In the 1976 paper they present a

Bayesian approach to forecasting which not only includes many con-

ventional methods, as presented before, but possesses a remarkable

range of additional facilities, not the least being its ability to

respond effectively in the start-up situation where no prior data

history (as distinct from information) is available. The essential

found ions of the method are:

(a) a para:metric (or state space) model, as distinct from

a functional model;

(b) probabilistic information on the parameters at any given

time;

(c) a sequential model definition which describes how the

parameters change in time, both systematically and as a result of

random shocks;

and

(d) uncertainty as to the underlying model itself, as be-

tween a number of discrete alternatives.

Kamat (1976) developed a smoothed Bayes control procedure for

controlling the output of a production process when the quality charac-

teristic is continuous with a linear shift in its basic level. The

procedure uses Bayesian estimation with exponential smoothing for

updating the necessary parameter estimates. The application of the

procedure to real life data is illustrated with an example. Applica-

tions of the traditional x-chart and the cumulative sum control chart

to the same data are also illustrated for comparison.

In Chapter Three of this dissertation we develop a Bayesian

model of nonstationarity for normal and lognormal processes. We build

our results directly on two papers, Winkler and Barry (1973) and Barry

and Winkler (1976). In the first paper they developed a Bayesian model

for nonstationary means in a multinormal data-generating process and

demonstrated that the presence of nonstationary means can have an impact

upon the uncertainty associated with a given random variable that has

a normal distribution. Moreover, the nonstationary model considered by

them seems to have more realistic properties than the corresponding

stationary model. For example, they found that in tlhe nonstationary

model the recent observations are given more weight that the distant

ones in determining the mean of the distribution at any given time,

and the uncertainty about the parameters of the process is never

completely removed. Barry and Winkler (1976) were concerned with the

effects of nonstationarity on portfolio decision. The use of a Bayesian

approach to statistical inference and decision provides a convenient

framework for studying the problem of changing parameters, both in

terms of forecasting security prices and in terms of portfolio decision

making. In this thesis a number of extensions to their results are

made, thereby removing some of the restrictiveness of their results,

and applications are considered in the areas of CVP analysis and life

testing.

CHAPTER TREE

NONSTATIONARITY IN NORMAL AND LOGNORMAL PROCESSES

3.1 Introduction

The normal distribution is considered by many persons an im-

portant distribution. The earliest workers regarded the distribution

only as a convenient approximation to the binomial distribution. However,

with the work of Laplace and Gauss its broader theoretical importance

spread. The normal distribution became widely and uncritically accepted

as the basis of much practical statistical work. More recently a more

critical spirit has developed, with more attention being paid to systems

of "skew (asymmetric) frequency curves". This critical spirit has per-

sisted, but is offset by developments in both theory and practice. The

normal distribution has a unique position in probability theory, and can

be used as an approximation to many other distributions. In real world

problems, "normal theory" can frequently be applied, with small risk of

serious erros, when substantially non-normal distributions correspond more

closely to observed values. This allows us to take advantage of the elegant

nature and extensive supporting numerical tables of normal theory. Host

theoretical arguments for the use of the normal distribution are based on

forms of central limit theorems. These theorems state conditions under

which the distribution of standardized sums of random variables tends to

a unit normal dist ribut ion as the number of variables in the sum increases,

that is with conditions sufficient to ensure an asymptotic unit normal

distribution.

The normal distribution, for the reasons exposed before, has

been widely used and enumerating the fields of application would be

lengthy and not really informative. However, we do emphasize that the

normal distribution is almost always used as an approximation, either

to a theoretical or an unknown distribution. The normal distribution

is well suited to this because its theoretical analysis is fully worked

out and often simple in form. Where these conditions are not fulfilled

substitutes for normal distributions should be sought. Even when nor-

mal distributions are not used results corresponding to "normal theory"

are often useful as standards of comparison.

The use of normal distributions when the coefficient of variation

is large presents many difficulties in some applications. For instance,

observed values more than twice the mean would then imply the existence

of observations with negative values. Frequently this is a logical absurdity.

The lognormal distribution, as defined in equation 2.2.20, is in at least

one important respect a more realistic representation of distributions

of characters that cannot assume negative values than is the normal distri-

bution. A normal distribution assigns positive probability to such events,

while the lognormal distribution does not. The use of the lognormal distri-

bution has been investigated as a possible solution to this problem [see

Cohen (1951), Calton (1879), Jenkins (1932) and Yuan (1933)]. In a

review of the literature Caddum (1945) found that the lognormal dis-

tribution could be used to describe several processes. In Chapter Two

we presented a list of some of the applications of this distribution

to real life problems. Among those applications we emphasized its

use in Cost-Volume-Profit analysis and in life testing models. Fur-

thermore, by taking the spread parameter small enough, it is possible

to construct a lognormal distribution closely resembling any normal

distribution. Hence, even if a normal distribution is felt to be really

appropriate, it might be replaced by a suitable lognormal distribution.

As was mentioned in Chapter Two, most research concerned with

the normal and lognormal distributions has considered only stationary

situations. That is, the parameters (known or assumed to be known)

and distributions used are assumed to remain the same in the future.

In this third chapter we intend to build a nonstationary model for

normal and lognormal processes from a Bayesian point of view. Section

3.2 sets the stage for the development of the nonstationary model. In

it, we describe essential features of the Bayesian analysis of normal

and lognormal processes including prior, posterior and predictive dis-

tributions. Two uncertainty situations are considered in this section;

in one the shift parameter, U is assumed to be unknown and the spread

parameter, o, is assumed to be known; and in the other, both parameters

are assumed to be unknown. In Section 3.3, we develop a particular non-

stationary model for the shift parameter of the lognormal distribution,

again under the same two uncertainty situations, and provide a com-

parison of the results with a stationary model.

3.2 Bayesian Analysis of Normal and Lognormal Processes

Before the last decade, most of the Bayesian research dealing

with problems ot statistical inference and decisions concerning a parame-

ter 0 assume that 0 takes on a single value; those models are called

stationary models. For example, 6 may represent the proportion of de-

fective items produced by a certain manufacturing process; the mean

monthly profits of a given company; the mean life of a manufactured

product and so on. In each case a is assumed to be a fixed but not known.

A formal Bayesian statistical analysis articulates the evidence of a

sample to be analyzed with evidence other than that of the sample; it

is felt that there usually is prior evidence. The non-sample evidence

is assessed judgmentally or subjectively and is expressed in proba-

bilistic terms, by means of: (1) a data distribution that specifies

the probability of any sample result conditional on certain parameters;

and (2) a prior distribution that expresses our uncertainty about the

parameters. When judgment in the form of the assessment of a likeli-

hood function to apply to the data is combined with evidence of a

sample, we have the likelihood function of the sample. The likelihood

function of the sample is combined with the prior distribution via

Bayes' theorem to produce a posterior distribution for the parameters

of the data distribution, and this is the typical output of a formal

Bayesian analysis. If we assume that the prior distribution, for the

parameters of the data distribution, is continuous then we may express

Bayes' theorem as

(3.2.1) t(6 x) = ( f(x)) o;

where
x denotes the vector of sample observations,

6 represents all the unknown parameters,

and
r represents the known parameters of the prior
distribution of 0.

We can interpret f(xlo) in two ways: (1) for given 6, f(xJo)

gives the distribution of the random vector 1; (2) for given x, f(xlO)

as a function of d, together with a] positive multiples, in the ususal

usage is the likelihood function of the sample.

The prior probability of the sample f(xrT) is computed from

(3.2.2) f(xII) = / f(0I1) f(xIo) de,
0

from which we see that f(xIT) can be interpreted as the expected

value of the likelihood in the light of the prior distribution. Alter-

natively, f(x[T) can be interpreted as the marginal distribution of

the random vector R with respect to the joint distribution,

(3.2.3) f(x, O T) = f(eO T) f(xle).

Since (3.2.2) can be computed in advance of the sample for any x,

we shall frequently refer to the marginal distribution of R as the

predictive distribution implied by the specified prior distribution

and datai distribtl ion.

If we have a posterior distribution f(ojx) and if a future

random vector "I is to come from f(w O), which may or may not be

the same data distribution as in (3.2.2), we may compute

(3.2.4) f(xl x) = I f(0lx) f(x 10) dO.
0

We refer to the distribution so defined as the predictive distribution

of a future sample implied by the posterior distribution. It must be

understood that (3.2.2) and (3.2.4) are but two instances of the same

relationship; sometimes it is worth distinguishing the practical prob-

lems arising when predictions refer to the present sample from those

arising in connection with predictions about a future sample; that is

a "not-yet-observed" sample. The revision of the prior distribution

gives the statistician a method for drawing inferences about 9, the

uncertain expression, quantity or parameter of interest, and for deci-

sions related to 0.

In general then we may say that the term Bayesian refers to

any use or user of prior distributions on a parameter space (although

there is some nonp.irametric Bayesian material also) with the associ-

ated application of Bayes theorem in the analysis of an inferential

or decision problem under uncertainty. Such an analysis rests on the

belief that in most practical situations the statistician will pos-

sess some subjective a priori information concerning the probable

values of the parameter. This information may often be reasonably

summarized and formalized by the choice of a suitable prior dis-

tribution on the parameter space. The fact that the decision maker

can not specify every detail of his prior distribution by direct asses-

sment means that t here will often be considerable latitude in the

choice of the family of distributions to be used, even though the

selection of a particular member within the chosen family will

usually be wholly determined by the decision maker's expressed beliefs

or betting odds. Three characteristics are particularly desirable for

a family of prior distributions:

(i) analytical tractability in three aspects; namely

a) it should be reasonably easy to determine the

posterior distribution resulting from a given prior and sample,

1) it should be possible to express in convenient

form the expectations of some simple utility functions with respect

to any member of it,

and

c) the family should be closed in the sense that if

the prior is a member of it, the posterior will also be a member of it;

(ii) the family should be rich, sot that there will exist a

member of it capable of expressing the decision maker's prior beliefs

or at least approximating them well;

and

(iii) it should be pajrametrizable in a manner which can

readily be interpr-etted, so that it will be easy to verify that the

chosen member of the family is really in close agreement with the

decision maker's prior judgments about 0 and not a mere artifact

agreeing with one or two quantitative summarizations of these judg-

ments.

A family of prior densities which gives rise to posteriors

belonging to the same family is very useful inasmuch as one aspect

of mathematical tractability is maintained, and this property has

been termed "closure under sampling". For densities which admit

sufficient statistics of fixed dimensionality, a concept to be

explained later, Raiffa and Schlaifer (1961) have considered a

method of generating prior densities on the parameter space that

possess the "closure under sampling" property. A family of such

densities has been called by them a "natural conjugate family".

To define the concepts of sufficient statistic and sufficient sta-

tistic of fixed dimensionality, consider a statistical problem in

which a large amount of experimental data has been collected. The

treatment of the data is often simplified if the statistician

computes a few numerical values, or statistics, and considers these

values as summaries of the relevant information in the data. In some

problems, a statistical analysis that is based on these few sum-

mary values can be just as effective as any analysis that could be

based on all observed values. If the summaries are fully informative

they are known as sufficient statistics. Formally, suppose that 6 is

a parametrr which takes a value in the space 0. Also suppose that x

is a randoin variable, or random vector, which takes values in the

sample space S. We shall let f(.0 0) denote the conditional proba-

bility density function (p.d.f.) of x when 0=00 (O0O). It is

assumed that the observed value of x will be available for making

inferences and decisions related to the parameter e. Denote any

function T of the observations x, a statistic. Loosely speaking,

a statistic T is called a sufficient statistic if, for any prior

distribution of 0, its posterior distribution depends on the ob-

served value of x only through T(x). More formally, for any prior

p.d.f. g(0) and any observed value xeS, let g(" x) denote the pos-

terior p.d.f. of 0, assuming for simplicity that for every value of

xeS and every prior p.d.f. g, the posterior g(' x) exists and is

specified by the Bayes theorem. Then it is said that a statistic

T is sufficient for the family of p.d.f.'s f(-10), 60O, if

g( -IX) = g(. x2) for any prior p.d.f. g and any two points xleS

and x2ECS such that T(x1) = T(x2).

Now, consider only data generating processes which generate

independent and identically distributed random variables ', 2, ...

such that, for any n and any (xl, x2 ... x ) there exists a suf-

ficient statistic. Sufficient statisticsof fixed dimensionality are

those statistics T such that T' (x x2 ... x ) = T = (T1, T ... T )

where a particular value T. is a real number and the dimensionality

s of T does nor depend on n. Independently of how many elements we

sample, only s stall istics are needed.

Raiffa and Schlaifer (1961) present the following method for

developing the natural runjugn te prior for a given likelihood function:

(i) Let the density function of a be g, where g denotes either

a prior or a posterior density, and let k be another function on 0

such that
k(O)
(3.2.5) g k(
fk(o) de
0
Then we shall write

(3.2.6) g(o) k(e)

and say that k is a kernel of the density of 0.

(ii) Let the likelihood of x given 0 be l(x 0), and suppose

that P and k are functions on x such that, for all x and 0,

(3.2.7) l(xlO) = k(xl|) P(x).

Then we shall say that k(xl0) is a kernel of the likelihood of x

given 0 and that P(x) is a residue of this likelihood.

(iii) Let the prior distribution of the random variable 0

have a density g'. For any x such taht l*(xlg') = J 1(x 0) g'(0) dO > 0,
0 ~
it follows from Bayes theorem that the posterior distribution of 0 has

a density g" whose value at (0) for the given x is

(3.2.8) g"(O0x) = g'(0) l(x|O) N(x)

where

N(x) = [ f g'(O) l(x O) dO]-1
0

(iv) Now let k' denote a kernel of the prior density of 0. It

follows from the definitions of k and 1 and of the symbol I that

the Bayes formula can be written,

(3.2.9) g"(9O x) = g'(6) 1(x|6) N(x)

= k'(0) [ k(o) de]1 k(x e) P(x) N(x)
0

g"(' x) k'(6) k(x|6),

where the value of the constant of proportionality for the given x,

(3.2.10) P(x) N(x) [ f k(6) dol-]

can always be determined by the condition,

(3.2.11) g"(Olx) do = 1, whenever the integral exists.

Before we begin our presentation of a basic Bayesian analysis

of normal and lognormal processes we want to emphasize that caution

should be exercised in the application of the method developed by

Raiffa and Schlailer, as is pointed out by Box and Tiao (1972). According

to them it is often appropriate to analyze data from scientific inves-

tigation on the assumption that the likelihood dominate the prior, for

two reasons:

(i) a scientific investigation is not usually undertaken unless

information supplied by the investigation is likely to be considerably

more precise than information already available, that is unless it is

likely to increase knowledge by a substantial amount. Therefore analysis

with priors which are dominated by the likelihood often realistically

represents the true inferential situation.

(ii) Even when a scientist holds strong prior beliefs about the

value of a parameter 0, nevertheless, in reporting the results it would

usually be appropriate and most convincing to his colleagues if he ana-

lyzed the data against a reference prior which is dominated by the like-

lihood. He could say that, irrespective of what he or anyone else be-

lieved to begin with, the posterior distribution represented what some-

one who a priori knew very little about 0 should believe in the light

of the data. Reference priors in general mean standard priors domi-

nated by the likelihood. [See Dickey (1973) for a general discussion

of Bayesian methods in scientific reporting.]

In general a prior which is dominated by the likelihood is one

which does not change very much over the region in which the likelihood

is appreciable and does not assume large values outside that range. We

shall refer to a prior distribution which has these properties as a

locally uniform prior. There are some difficulties, however, associated

with locally uniform priors. The choice of a prior to characterize a

situation where "nothing" (or, more realistically, little) is known a

priori has long been, and still is, a matter of dispute. Bayes tenta-

tively suggested that where such knowledge was lacking concerning the

nature of the prior distribution, it might be regarded as uniform. There

is an objection to Bayes postulate. If the distribution of a continuous

parameter 0 were taken to he locally uniform, then the distribution of

log or some other transformation of (which might provide equally
log 0, 0 or some other transformation of 0 (which might provide equally

sensible bases for parametrizing the problem) would not be locally

uniform. Thus, application of Bayes' postulate to different trans-

formations of 0 would lead to posterior distributions from the same

data which were inconsistent with the notion that nothing is known

about 0 or functions of 0 This argument is of course correct, but

the arbitrariness of the choice of parametrization does not by it-

self mean that we should not employ Bayes postulate in practice.

Box and Tiao (1972) present an argument for choosing a par-

ticular metric in terms of which a locally uniform prior can be

regarded as noninformative about the parameters. It is important to

bear in mind that one can never be in a state of complete ignorance;

further, the statement "knowing little a priori" can only have mean-

ing relative to the information provided by the experiment. A prior

distribution is supposed to represent knowledge about parameters

before the outcome of a projected experiment is known. Thus, the main

issue is how to select a prior which provides little information rela-

tive to what is expected to be provided by the intended experiment.

3.3 Nonstationary Model for Normal and Lognormal Means

It was emphasized in Section 2.3 that for many real world

data generating processes the assumption of stationarity is question-

able. Random parameter variation could be a reasonable assumption when

we are concerned with life testing models or with economic variables.

For example, in life testing models, when it is assumed that the life

of certain parts follows a lognormal distribution, the stationarity

assumption could be expected to hold over short periods of time; but

in most cases it would be expected that for a lengthy period, statio-

narity would be a doubtful assumption. Similarly in other areas like

Cost-Volume-Profit analysis it is doubtful that the stationarity

assumption will hold over long periods of time. Variables like sales,

costs, and contribution margin are affected by economic, political

and environmental factors. In particular it was pointed out that we

are interested in gradual changes, the effects of which are not perfectly

predictable in advance for a particular period.

If a data generating process characterized by some parameter

6 is nonstationary, then it is potentially misleading to make inferences

and decisions concerning 0 as if 6 only took on a single value. Instead

we should he concerned with a sequence 61, 62, ... of values of 6 cor-

responding to different time periods, assuming the characteristics of

the process may vary across Lime. Several methods have been proposed

to study stochastic parameter variation [see Chernoff and Zacks (1964)

and Harrison and Stevens (1976)]. Some have claimed that a reasonable

approach to the effects of gradual change might be to model the para-

meters of nonstationary distributions as if they undergo independent

random shifts through time [see Barry (1976), Carter (1972), and

Kamat (1976)]. Specifically they suggest the use of a model that

assumes that the mean of the distribution has a linear shift. In those

papers, it is clearly demonstrated that when it is assumed that the

process represented by the model is normal, this linear random shift

,model allows anal ytical comparisons to be drawn if it is assumed that

the successive increments in the process mean are drawn independently

from a normal population with mean u and variance p. We intend to

use the same approach in this dissertation. Two cases are considered:

11 unknown and o2 known; and both 1 and o2 unknown.

3.3.1 P is Unknown and 02 is Known

For a process that has a normal density function with unknown

parameter f, Raiffa and Schlaifer (1961) show that the natural conju-

gate prior is normal with parameters m' and o2/n'. (See Appendix I

for the details of their exposition.) From the prior distribution on

00 and with a sequence of n independent observations (x, x2, ... xn

from the normal process under consideration [N(p,o2)], the posterior

distribution in period zero is obtained. If the sample yields sufficient

statistics m and n, then the posterior distribution is normal with para-

meters n1 and m" given by

(3.3.1) n" = n + n,

and

(3.3.2) mi = (n0 mo + n m)/(n + n).

If the mean of the distribution does not change from period to period

except by the effect of the sample information then each posterior can

be thought of as prior with respect to the following sample. Thus, the

posterior distribution on 00 is the prior distribution on i0 ; i.e.

(.3.3.) 1I (i(0 i o2/n1 ) = f (Fp o2/1 )

where

(3.3.4) m = m' ,

and

(3.3.5) n = n
0 1

In general, if we assume that a fixed sample of size n is employed

every time a sample is taken and if we assume that the mean is sta-

tionary except by the effect of the sample information, then in any

given period t the posterior distribution is normal with parameters

n" and m" given by,
t t

(3.3.6) n" = n' + n
t t

and

(3.3.7) m" = (n' m' + n m)/(n' + n).
t tt t

This inferential model is called a stationary model since it assumes

that neither the distribution nor the parameters change from period

to period. In this case it assumes that ut takes on the same value

in every period and that f'(u ) represents the information available

about that value as of the start of the t-th period.

Suppose now that the process generating the observations un-

dergoes a mean shift between succesive periods. In particular infer-

ences about the mean of a normal process are considered when the para-

meter I shifts from period to period, with the shifts governed by an

independent normal process. Formally, consider a data generating pro-

cess that generates n observations tl x t2' ..., xtn during time

period t according to a normal process with parameters pt and ao.

Assume that the parameter o" is known and does not change over time,

whereas pt is not known and may vary over time. In particular, values

of the parameter for successive time periods are related as,

(3.3.8) t l = t + %t+1 t = 1, 2, ...

where t+ is a normal "random shock" term independent of p with

known mean u and variance o2.That is t behaves as a random walk.
e t

The mean in any period t is equal to the mean in the previous period

plus an increment e, which has a normal distribution, with known

mean and variance.

Before the sample is taken at time t, we assume that a prior

density function could be assessed that represents judgment (based

on past experience, past information etc.) concerning the probabilities

for the possible values of t. If the prior distribution of pt at the

beginning of time period t is represented by f'(t ), and a sample of

size nt during period t yields xt = (t ... xtn), then the prior

distribution of p can be revised. Furthermore at the end of time

period t (the beginning of time period t+l), the data generating pro-

cess is governed by a new mean Dt+ so it is necessary to use the

posterior distribution of t and the relation (3.3.8) to determine

the prior distribution of pt+

In order to determine the distribution of the parameter pt+l

a wel l known tlheoim c could be used. It says that the convolution g(z)

of two normal dist ribut ions witli parameters (pt,o2) and (p2,02)

gives a distribution which is normal with mean (ip + 112) and variance

(02 + o2), i.e..

(3.3.9) g(z) = fN(ZIPl + P2' + o2)'

[see Mood et. al. (1974)]. Thus the distribution of t+ is normal,

i.e.,

(3.3.10) f( t+ m" + u, (02/n") + o2) -,< t+ <'
N C+1 t t e t+1
-m< m" + u t

(o2/n") + 02 >0.
t e

We could find a simpler expression if we realize that, since o2 and

a2 are positive, there must exist n such that,
s

(3.3.11) 02 = o2/n
e s

or n = 02/o2
s e

In other words, the disturbance variance is a multiple of the pro-

cess variance. The prior distribution of the mean after t periods then

simplifies to

(3.3.12) fN(t+Im + u, 02(n" + s )/n' ns
f t t !!

or

(3.3.13) f'( t ln' 0 2/ )
where' t+
where

m'+l = mi' + u
t+i 1: '

( .3.14)

and

(3.3.15) ni = i n / (n( + n ) <] n"
L+1 t s t s t

The inequality stated above can be interpreted as showing that the

presence of nonstationarity produces greater uncertainty (variance)

at the start of period L+L than would be present under stationarity

because in the stationary case n' = n". If we assume that a change
t+I t
in the mean occurs between every two consecutive periods then we could

repeat the previous procedure each time a change occurs to determine

the new prior distribution.

For a process that has a lognormal density function as defined

in (Al.14), it was shown in Appendix I that, when the unknown parame-

ter is p, the natural conjugate prior is normal. Thus, the revision

of the prior distribution in any given period is identical to the revi-

sion in the normal case [see equations (3.3.6) and (3.3.7)] except that m

is defined as the sample mean of the natural logarithms of the observed

x values. Furthermore the procedure presented before to represent

changes in the mean, F, of the normal distribution can be used to model

changes in the shift parameter p of the lognormal distribution. The

normality of the natural conjugate prior, in this case, allows us to

use the formulas (.3..8)-(3.3.15) to study the behavior of the prior dis-

tribution of p alter t periods of time.

Since the variance V(x) of the lognormal random variable x is

1 function of i and 6" in the lognormal case, nonstaLionarity in 0

means that both the mean and the variance of x are nonstationary, so

that the lognormal case provides a generalization of the normal results.

3.3,2 p and 62 Both Unknown

The results of the previous section can he extended to the case

of unknown mean and variance. The joint natural conjugate prior density

function for p and 02 is a normal-gamma-2 functions, as was shown in

Appendix I, given by

(3.3.16)
d'
-1
n' d'v' d'v' 2 d'v'
/n' exp[- -z2(U-m')] exp[- -7-T2--] [-
' (',a2 m',v',n',d') = 2 2-G 2
oN-y /2- F(d'/2)

Given a prior from this family and assuming that information is

available from a normal (or lognormal) process through a sample of obser-

vations xl, x,5 ... x it is possible to obtain a posterior distribution

of the two parameters D and d2. It was shown in Appendix I that the pos-

terior distribution is also normal-gamma-2, i.e., f" (p,62 m",v",n",d")
N-y-2
where

(3.3.17) m" = (n'm' + n m)/(R' + n)

(3.3.18) v" = [d'v' + n'm'2 + dv + nm2 n"m"2]/(d' + n)

n" = n' + n

(3.3.19)

and

(3.3.20) d" = d' + n,

It is clear from (3.3.16) that the joint distribution of p

and a2 is the product of two marginal distributions, i.e.,

(3.3.21) f" ( ,2 im",v",n",d") = f"( 12 ,n",m") ft"(C2v",d")
N-y-2

The marginal density of 82 does not depend on . Now consider the case

of nonstationary p as in the previous section. The independence of the

marginal distribution of o2 from p will be an important factor in our

results below.

At the end of period t (the beginning of time period t+l) the

posterior distribution of p and 02 could be used in conjunction with

the relation between pt and the random shock et+l to get the joint

prior distribution at the beginning of period t+l. As before,the random

shock model to be considered is pt = p + e We make the assump-
t+l t t+i
tion that although 02 is unknown, it is known that e 's variance,
t
o2, is 1/n times the unknown process variance, 02. As before, assuming
e s

that P has a posterior distribution with parameters (m",'2/n") and that

& is distributed normally with parameters (u,o2/n ) it was shown in
s
Appendix I that the convolution z (z = u + e) has a conditional density

given by

(3.3.22) g(z) = f"(zim" + u, d2[(l/n") + (1/n )]).

Note that this density is conditional on d2, as is the conjugate

prior of f. Thus, the prior density of pt+l' at the beginning of period

t+l after the random shock has occurred, is given by

(3.3.23) f'(pt+llm" + u, 2 [("s + nt)/n't ns

Since 62 is assumed constant, f2 (a2) does not change but

equals the posterior distribution at the end of period t. Hence,the

joint distribution at the beginning of period t+l is given by

(3.3.24) f- 2(t+1,6) = Nf (t+llmt + u,2[(ns+ nns) ) f (62Idtv )

If we let

(3.3.25) m' = m" + u,
mt+1 t

(3.3.26) n' = n" n/(n + n")
t+1 t s s t

(3.3.27) d+1 =t'

and

(3.3.28) v'+ = v"
t+1 t '

then the distribution of j and OQ could be written as

(3.3.29) f' ( t+l,02) = '( t+lm' 2/nt') f'2 (62d v'
N-y2(t+1 2 N t+1 t+1, t+1 f-2( t+1, t+1).

The revision could be continued since the prior distribution

at the beginning of period t+l is still a normal-gamma-2 distribution.

At any Lime t, the process mean is not known with certainty, but the

information from the samples collected up to time t provides an indi-

cation of t. Before lhe sample is taken at time t, we assume that

one is capable of assessing a prior density function that represents

our judgment (based on past experience, past information, etc.) con-

cerning the probabilities for the possible values of pt and 6'. In

effect, one view; (Pt,0 ) as a pair of random variables to which we

have assigned a probability density function; in this case a normal-

gamma-2 with parameters m', n', v' and d'. The sample results at time

t can be described in terms of the sufficient statistics m,, nt, v

and d ; sample mean, sample size, sample variance and degrees of free-

dom needed to determine vt, respectively. Using these sample results,

a new posterior distribution could be obtained which is normal-gamma-2.

The tractability of the model is maintained when a natural conjugate

prior is used and ai shift model of the form (3.3.8) is assumed for the

changes of the parameter P between two consecutive periods. Hence,

after t periods of time the joint distribution of p and 2 is norma-

gamma-2; that is,

(3.3.30) fN_-y2 (t+ i021 m,+l n'+ dt+' vt+l) '

where

(3.3.31) d' = d' + (t)n
t+l 1

(3.3.32) n' = (n' + n)ns/[(n' + n) + n ,
L+1 t s s

(3.3.33) v' = [d'v' + n'm'2 + dv + nm2 + n"m"2]/td' + n]
Lt t t t t t t t

and

(3.3.34) m = (n'm' + nm)/(n' + n).
t3+1 tt t

In this manner, a sequence of prior and posterior distributions for

successive pt may be obtained as successive values of the random vector

g = (xIt' .. xt ) are observed.

For the process that has a lognormal density function as defined

in (Ai.14), it was shown before that when both parameters are unknown

the joint natural conjugate prior is normal-gamma-2. Thus, the revision

of the prior distribution in any given period is identical to the revi-

sion in the normal case. Furthermore the procedure presented previously

to represent changes in the mean, P, of the normal distribution could

be used to model changes in the shift parameter of the lognormal.

The fact that both normal and lognormal distributions have a joint

natural conjugate prior which is normal-gamma-2 allows us to use the

formulas (3.3.30 3.3.34) to study the behavior of the prior distri-

bution of P and 62 after t periods.

3.3.3 Stationary Versus Nonstationary Results

Stationary conditions, in the context of our discussion, imply

that there is no shift in the mean, i, of the distribution; that is,

= 0 and consequently u and 92 are both zero. Successive values of U
t e
are the same across t ime, i.e., Iii= P2= ... For the case when
1 2 t

only Ti is unknown,this implies that equation (3.3.10) becomes,

(.3.3.5) f,(p't+ll m + 0, (,2/np) + 0)

or

(3.3.36) f [Ot+l m"' (o2/n")].

Under stationarity, then, the prior distribution of Dt+, at the start

of period t+l is the same as the posterior distribution of jt at the

end of period t. In the case of nonstationarity with no drift, u=0;

in other words, the distribution of e is normal with mean 0 and vari-

ance a2. For this case it is clear that for a given posterior distri-

bution of t at time t, the only difference between the prior dis-

tributions of 0it+ under stationarity (see equation 3.3.36) and the

prior distribution of t+ under nonstationarity (see equation 3.3.10)

is the variance term. The prior variance of .t+l under stationarity

is,

(3.3.37) VarS t+1) = o2/n'+1 = 02/n't;
(3.3.37) Var 2 n+ G

whereas the prior variance of Ot+l under nonstationarity is,

(3.3.38) VarN ( ) = 02/n+ = (2/n") + (02/n )

= o2[(1/nt) + (1/n)].

As expected, the incorporation of the nonstationary condition has

caused an increase in the variance of the prior distribution. The

variance increased by an amount oz/ns; that is,by an amount equal

to the variance of the distribution of successive increments in the

process mean. For the stationary case

(3.3.39) [n' = [/n"]
t+1 t

and for the nonstationary case,

-I ( / t ( / )
(3.3.40) [nt' ] = [(1/n ) + (1/n )].
t+1 t s

Thus, equivalently, we could say that for a given posterior distri-

bution of pt at time t, the only difference between the prior distri-

bution of 0Jtl under stationarity is that the term n' is larger
t+l
with the stationary condition. When u+0, m' is always changing and,
t
therefore, there is a difference in mean and variance.

Stationary conditions, in the case when both 0 and 82 are

unknown, imply that in any given period t+l the joint prior density

for P and 62 is a normal-gamma-2 of the form given in equations

(3.3.30 3.3.34). That is,

(3.3.41)

fN-y-P( 2 t82v' n'd'+ ) = +l(P +ll'-n f;-2( 21d(2 d' v' )
t-It2it+1 ,tt+] ,t+l ,n tI+ N + t+t
='fl 'nt+1

where

(3.3.42) m' = m" ,

t+13 t

(3.3.43) v' = v" ,
t+1 t

(3.3.44)

,I
11t- = cI

and

(3.3.45) d' = d"
+ t

Under stationarity, then, the joint prior distribution of p and 82

at the start of period t+l is the same as the posterior distribution

of pt and d2 at the end of period t. Since the distribution of 62

does not depend on 5, only on the parameters d and v, we could model

changes in p. These changes in the mean only affect the function

f-'(+l mti+,' 2/n'+ ), in equation (3.3.41). In fact, the effect

of the nonstationarity assumption on f'(p +) is identical to the

effect of nonstationarity over the prior distribution in the case

when only j was the unknown parameter. In the case of nonstationarity

with no drift, i.e., u=0, for a given posterior distribution of jt and

62 at time t, the joint prior density function for 0 and 62 is similar

to the stationary counterpart, as given in equation (3.3.41), except

for the fact that the variance of f'N ,, t,' 62/n' ) is larger
fN t+l
than the variance of f(p +l m'+l, 62/n' ) in the stationary case.

In other words a2/n1' in the stationary case is smaller than 82/n'
L+l t+1
in the nonstationary case.

The nonstationarity assumption also affects the predictive

distribution. For the case when p is the unknown parameter and the

data generating process is normal, assume that after t periods we have

.1 posterior distribution f"(p ) which is normal with mean m" and
t L L
variance o '/11'. Ttie predictive distribution at the end of period t

was sliown in equat ion (A1.12) to be normal with mean,

(3.3.46) E (x ) = m"
t t t

and variance

(3.3.47) Var (x) = 02(1 + n")/n"] = 2[1 + (1/n")].
t t t t t

If the process is stationary then the predictive distribution of the

random variable of interest at the beginning of period t+l is the same

as the distribution we had at the end of period t, i.e., N(m",o2[(1+n")/n").
t t t

However if we assume the nonstationary condition, the prior distribu-

tion of P at the start of period t+l has a different mean and a dif-

ferent variance. Consequently the predictive distribution changes in

mean and variance between consecutive time periods. In other words

E t(xt ) is always changing depending on the stochastic change of

the mean pt+. In the case of nonstationarity with no drift, i.e., u=0,

for a given posterior distribution of pt at time t, the only differ-

ence between the predictive distribution of xt+I under stationarity

and the predictive distribution of xt+l under nonstationarity is the

variance term. The variance of xt+ under stationarity, at the start

of time period t+l, is

(3.3.48) Var (x ) = o2[(l+n' )/n' I = o2[1+(1/n )].

It was stated previously that the parameter n +1 is smaller when

is unknown and nonstationary than when p is unknown but stationary.

Hence, as expected, the variance of the predictive distribution,

Var t+(xt+ ), is larger when p is nonstationary. This has some

implications for the determination of prediction intervals; which

we will discuss in detail in Chapter Four. Nonstationarity implies

greater uncertainty, which is reflected by an increase in the mea-

sure of uncertainty, variance.

For the case when both p and 62 are the unknown parameters

and the data generating process is normal, assume that after t

periods we have a posterior distribution f"( ,t'2) which is

normal-gamma-2 with parameters m", n", v" and d". The predictive
t t t t
distribution at the end of period t was shown in equation (AI.33)

to be Student with mean,

(3.3.49) E (x ) = m" d" > 1,
t t t t

and variance

(3.3.50) Var (x ) = [v" (n'+ ) /n"] [d"/(d" 2)], d" > 2.
L t t L t t t t

Again, if the process is stationary then the predictive distribution

at the beginning of period t+1 is the same as the distribution that

we had at the end of period t, i.e., ST (m", [v"(n"+l)/n"][d"/(d" 2)]).
t t t t t t

When we assume the nonstationary condition, the.joint prior

distribution of 0 and 82 at the start of period t+l changes from its

original form at the end of period t. The specific random model we

are assuming causes the parameter m and n of the distribution of

U to change from lhe end of period t to the start of period t+l.

Therefore the predictive distribution f' (x ) has a different
t+1 t+1
mean and variance than t"(x ). In the case of nonstationarity with
no drift, i.
no drift, i.e., u=0, for a given posterior distribution of 1t and 62
t

at time t, the only difference between the predictive distribution of

xt+1 under statioiarity vis-a-vis nonstationarity is the variance term.

Observing equation (3.3.50) closely we note that the effect of nonsta-

tionarity is the same as in all previous cases; that is the parameter

n+, is smaller when p is nonstationary and therefore the variance is

larger. In this case since both p and a2 are unknown, at the end of

period t our estimate of the variance is v" which includes all the
t
information that we have available at the time including sample in-

formation.

A comparison of stationary versus nonstationary results when

the data generating process is lognorma] moves along the same lines as

the normal process does. For the case where the unknown parameter is

1, the nonstationarity condition causes an increase in the variance

and in the mean of the normal prior distribution which causes an

increase in the mean and variance of the lognormal predictive distri-

bution. Similarly, for the case when both parameters are unknown the

condition causes an increase in mean and variance in the prior distri-

bution of p and a change in the joint prior distribution of Q and a2

which affects the logStudent predictive distribution. The logStudent

predictive distribution has infinite mean and variance which are not

affected by the nonstationary condition.

3.4 Conclusion

In this chapter we modeled nonstationarity in the mean of

normal and lognormal processes under two uncertainty assumptions,

The model is built upon the Bayesian analysis of normal processes

of Raiiftli andi Schlaifer (19(1) and upon tile analysis of nonstationary

means of normal processes, for unknown i, of Barry (1973). We extended

the nonstationary results of Barry (1973) to the lognormal distribu-

tion. The variance of the lognormal distribution is given by

(3.4.1) Var(x) = w(w-l) e2 ,

where w = exp(o2).

Since V(x) is a function of 11 and o2 in the lognormal case, nonsta-

tionarity in D means that both mean and variance of x are nonsta-

tionary, so that the lognormal case provides a generalization of the

normal results. Furthermore, we developed the nonstationary model

for tie mean of normal and lognormal processes for the case when both

parameters, j and 02, are unknown. For each group of assumptions we

noted that, in every time period t, the uncertainty is never fully

eliminated from tle model.

In Chapter Two we emphasized that the exponential distri-

bution was often used to represent life testing models. All the

research in the area of life testing where this distribution has

been used has assumed stationary conditions for the parameters of

the model and for the model itself. Appendix II shows the Bayesian

modeling of nonstationarity for the parameters of an exponential dis-

tribution using random shock models. Only under very trivial as-

sumptions does the analysis yield tractable and consequently useful

results. On the other h ;nd, as was shown in this chapter, tile normal

and lognormal distributions provide results that are especially

tractable.

In any given period t, the prior, posterior and predictive

distributions depend on the parameters, m and n when only p is
t t

unknown; and on the parameters m nt, vt and d when both p and

o2 are unknown. Under the nonstationarity conditions, these para-

meters change from period to period not only because new information

becomes available through the sample, but because of the additional

uncertainty involving the shifts in the parameter p. To make better

use of these distributions the decision maker must know how they are

evolving through time. Management requires realistic and accurate

information to aid in decision making. For instance the decision

maker can be interested in knowing how the variance of the distri-

bution of the mean, p, changes across time. Furthermore, since one

of the objectives of the user of the distribution is to construct

prediction intervals for the process variable he can be interested

in knowing how the variance of the predictive distribution behaves

as the number of observed periods increases. We will address this

problem in detail in Chapter Four through the study of the limiting

behavior of the parameters m n t, vt and d In addition, attention

will be focused on the methods of constructing prediction intervals

for the normal, Student, lognormal and logStudent distributions

under various uncertainty conditions.

CHAPTER FOUR

LIMITING RESULTS AND PREDICTION INTERVALS FOR NONSTATIONARY

NORMAL AND LOGNORMAL PROCESSES

4.1 Introduction

In Chapter Three we emphasized that for many real world data

generating processes the assumption of stationarity is questionable and

stochastic parameter variation seems to be a reasonable assumption. If

a data generating process characterized by some parameter is nonstation-

ary, then it is potentially misleading to make inferences and decisions

concerning the parameter as if it only took on a single value. We should

be concerned with a sequence of values of the parameter corresponding to

different time periods. It was shown in Chapter Three that if we use a

particular stochastic model we can model nonstationarity for the shift

parameter of normal and lognormal processes from a Bayesian viewpoint,

under two uncertainty conditions, and that we can obtain tractable

results. In particular, values of the parameter for successive time

periods are assumed to be related as

(4.1.1) Pt+ = ~ + et+1 t = 1, 2, ... ,

where e+l is a normal "random shock" term independent of t with known

mean u and variance o2. The mean in any period t is equal to the mean
e

in the previous period plus an increment e, which has a normal distri-

bution, with known mean.

Comparing the stationary with the nonstationary processes we

pointed out that when the data generating process is normal or log-

83

normal and the unknown parameter is p, the nonstationary condition

causes in any given period t an increase in the variance of the nor-

mal prior distribution. This causes an increase in the mean of the nor-

mal predictive distribution for normal processes and causes an increase

in the mean and variance of the lognormal predictive distribution for

lognormal processes. When both parameters, p and o2, are unknown a

similar result is found for the prior and predictive distributions of

the normal and lognormal data generating processes.

The results discussed in Chapter Three have to do with the

period to period effects of random parameter variation upon the prior

and predictive distributions. However, the asymptotic behavior of the

model has important implications for the decision maker. For instance,

when only p is the unknown parameter, under constant parameters uncer-

tainty about p eventually is eliminated since n' increases without

bound and the sequence of prior variances (o2/nd) converges to zero.

Hence the distribution of t eventually will be unaffected by further

samples. On the other hand, shifting parameters could increase the uncer-

tainty under which a decision must be made since it reduces the infor-

mation content that past samples offer for the actual situation. Increases

in uncertainty, caused by stochastic parameter variation, have important

implications for the decision maker since his decisions depend upon

the uncertainty under which they are made. Similarly, random parameter

variation produces important differences in the limiting behavior of

the prior and predictive distributions when i and o2 are the unknown

parameters. In Section 4.2 we study the limiting behavior of the param-

eters m', v, nt, and d' of the prior and predictive distributions for

the normal and lognormal data generating processes. In addition we dis-

cuss the implications of these limiting results for the inferences and

decisions based on the posterior and predictive distributions.

In any period t, all the information contained in the initial

prior distribution and in subsequent samples is fully reflected in the

posterior and the predictive distributions. In some applications, partial

summaries of the information are of special importance. One important

way to partially summarize the information contained in the posterior

distribution is to quote one or more intervals which contain a stated

amount of probability. Often the problem itself will dictate certain

limits which are of special interest. A rather different situation

occurs when there are no limits of special interest, but an interval

is needed to show a range over which "most of the probability lies".

One objective of this thesis is to develop Bayesian prediction

intervals for future observations that come from normal and lognormal

data generating processes. In particular, we are interested in most plau-

sible Bayesian prediction intervals of cover P as were defined in Section

2.2. In Section 4.3 we discuss the problem of constructing prediction

intervals for normal, Student, lognormal and logStudent distributions.

It is pointed out that it is easy to construct these intervals for the

normal and Student distributions but that it is rather difficult for

the lognormal and logStudent distributions. An algorithm is presented

to compute the BayesLan prediction intervals for the lognormal and log-

Student distributions. In addition, we discuss the relationship that

86

exists between Bayesian prediction intervals under nonstationarity

and classical certainty equivalent and Bayesian stationary intervals.

4.2 Special Properties and Limiting Results
Under Nonstationarity

4.2.1 Limiting Behavior of m' and n' When P is the Only Unknown Parameter
t t

For a process that has a normal density function with unknown

parameter p, Raiffa and Schlaifer (1961) show that the natural conjugate

prior distribution is normal with parameters m' and o2/n'. In Section

3.3 we pointed out that if the mean, p, of the data generating process

does not change from period to period except by the effect of the sample

information, then each posterior can be thought of as a prior with

respect to a subsequent sample. In general, if we assume that a sample

of size nt is employed every time a sample is taken [which yields a
n
statistic m = ( E x ./n)] and if we assume that the mean p is sta-
i=l
tionary then in any given period t the posterior distribution of p

is normal with parameters n" and m" given by
t t

(4.2.1) n" = n' + n
t t t
and

(4.2.2) m" = (n' m' + n m )/(n' + n).
t t t t t t

In order to study the limiting values of n' and m' under sta-
t t
tionary conditions, we have to characterize the posterior and predictive

distributions after t periods of time have elapsed. Since the limiting

results under nonstationary means will be based on a fixed sample size

each period, we will make the same assumption for the stationary lim-

iting results, that is n n, Vt, In period one, for a process that has

a normal density function with unknown parameter p,i.e., fN(xl1p),

the natural conjugate prior is normal with mean m' and variance o2/n,

i.e.,fN (Pim{,G2/n{). If a sample of size n from a normal process yields

the sufficient statistics mI and n, then the posterior and predictive

distributions at the end of period one are given by

(4.2.3) f" [pl(n'm{ + nm )/(n'+ n),o2/(n{+ n)] = f"(Plm1',2/n")

or

= fN21m2,o2/n) ,

and

(4.2.4) fN(xl im, 2(l + n/n")

respectively.

In period two, if a sample is taken from a normal process that

yields the sufficient statistics m2 and n then the posterior and predic-

tive distributions at the end of the period are given by,

(4.2.5)

f [I2 [n1ml + n(ml+ m2)]/(n'+ 2n). o2/(n' + 2n)] = f"(21m'0, o2/n)

or

= f 3(lm3, 02/n'),

and

(4.2.6) fN(x2 m2, 02(1 + n2)/n')
1' 2 2"

respectively.

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BAYESIAN MODELING OF NONSTATIONARITY IN NOK^L\L AND LOGNORMAL PROCESSES WITH APPLICATIONS IN CVP ANALYSIS AND LIFE TESTING MODELS By JORGE IVAN VELEZ-AHOCHO A DISSERTA'l ION PRESENTED TO THE GRADUATE COUNCIL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILISOPHY INIVERSITY OF FLORIDA 1978

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Copyright 1978 by Jorge Ivan Velez-Arocho

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lis dissertation stands as a symbol of love to my wife, Angie, and to my daughter, Angeles Maria, without v;hose understanding, patience and willingness to accept sacrifice this investigation would have been quite impossible.

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ACKNOWLEDGMENTS I would like to acknowledge my full indebtedness to those people \A\o gave their interest, time and effort to making this dissertation possible. To Dr. Christopher B. Barry who has been my advisor and my friend, 1 wish to express my gratitude and deepest appreciation for the su[)port lie has given me throughout the development of this study. He critized but tolerated my mistakes and encouraged my good performance. His intelligent guidance, extraordinary competence, and friendly attitude have been a source of inspiration and encouragement for me. I am especially grateful to Dr. Antal Majthay for his sincere advice and assistance during the supervision of my doctoral program and the preparation of this dissertation. I admire and am inspired by bis unreserved dedication to excellence in education. He will always be remembered as one of the most valuable models of excellent teaching. The other members of my committee. Dr. Tom Hodgson and Dr. Zoran Pop-S tojanovic have each in his own way contributed to the successful completion of this work. Appreciation is extended to each for his individual efforts and expressed concern for my progress. Although not on my committee, I would also like to express appreciation to Dr. Gary Koehler, whose support iind encouragement came when they were badly needed . To Omar Ruiz, Dean of the School of Business Administration of tliLUniversity of VuitUo Rico at Mayaguez, I am particularly grateful iv

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for his understanding, confidence and cooperation during my leave of absence from that institution. Completion of this study was only possible because of the combined financial support of the University of Puerto Rico, the University of Florida and Peter Eckrich and Sons Co.. Their (.ontinuous support is sincerely appreciated. I am indebted to Dr. Conrad Doenges, Chairman of the Department of Finance of the University of Texas at Austin, for his interest and help and to the many members of the Finance faculty for their interest during my period of research at the University of Texas. Special thanks go to Nettie Webb for her warm friendship and continuous secretarial assistance to my wife. It is difficult to adequately convey the support my family has provided. My parents, Jorge Velez and Elba Lucrecia Arocho , and my brothers and sisters provided understanding and moral assistance for which I will alvjays be grateful. Their high expectations and constant encouragement have been a powerful factor in shaping my desire to pursue this degree. Most of all a gratitude v^fhich cannot be expressed in words goes to my loving wife, Angle, for her patience and persistance in typing this dissertation and for her wonderful attitude throughout the entire arduous process. V

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TABLE OF CONTENTS Page ACKNOWLEDGEMENTS iv LIST OF APrENDIX TABLES ix LIST OF FIGURES x ABSTRACT xi Chapter ONE INTRODUCTION 1 1.1 Introd>ictlon 1 1.2 Sur.imary of Results and Overview of Dissertation A TWO SURVEY OF PERTINENT LITERATURE 9 2.1 Cost-Volune-Prof it (CVP) Analysis 9 2.2 Life Testing Models 17 2.2.1 Introduction 17 2.2.2 Some Common Life Distributions 22 2.2.3 Traditional Approach to Life Testing Inferences 29 2.2.4 Bayesian Techniques in Life Testing 33 2.3 Modeling of Nonstationary Processes 41 THREE NONSTATTONARITY IN NORMAL AND LOGNORMAL PROCESSES 51 3 . 1 Introduction 51 3.2 Bayesian Analysis of Normal and Lognormal Processes.... 54 3.3 Nonstationary Model for Normal and Lognormal Means 63 3.3.1 p is Unknov>m and a^ is Known 65 3.3.2 jj and a'^ Both Unknown 70 3.3.3 Stationary Versus Nonstationary Results 74 3 . 4 (Conclusion 80 FOUR LIMITING RESULTS AND PREDICTION INTERVALS FOR NONSTATIONARY NORMAL AND LOGNORMAL PROCESSES 83 4.1 Ini rcnhu1 i on 83 vi

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Chapter Page 4.2 Special Properties and Limiting Results Under Nonstationarity 86 4.2.1 Limiting Behavior of m' and n' Wlien y\ is the Only Unknown Parameter 86 4.2.2 Limiting Behavior of m' n' v' and d' When Both Parameters \^ and a*^ are Unknown 95 4.3 Prediction Intervals for Normal, Student, Lognormal and LogStudent Distributions 103 4.4 Conclusion 117 FIVE NONSTATIONARITY IN CVP AND STATISTICAL LIFE ANALYSIS 119 5.1 Introduction 119 5.2 Nonstationarity in Cost-Volume-Profit Analysis 120 5.2.1 Existing Analysis 120 5.2.2 Nonstationary Bayesian CVP Model 122 5.2.3 Extensions to the Nonstationary Bayesian CVP Model 1 36 5.3 Nonstationarity in Statistical Life Analysis 140 5.3.1 Existing Analysis 140 5.3.2 A Life Testing Model Under Nonstationarity 141 5.4 Conclusion 148 SIX CONCLUSIONS, LIMITATIONS AND FURTHER STUDY 150 6. 1 Summary .' 150 6.2 Limitations 152 6.3 Suggestions for Further Research 155 APPENDIXES I Bayesian Analysis of Normal and Lognormal Processes 160 IT Nonstationary Models for the Exponential Distribution 172 III Algor it 1)111 to Peterriiine Predict; ion TntervaJs for Lt)gnormnl and LogStudent Distributions 185 vii

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Page LIS'l' OF REFERENCl'S 198 BIOGRAPHICAL SKETCH 213 VI 1 1

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Table LIST OF APPENDIX TABLES Page 1. "redictlve Intervals for Some Lognormal Predictive Distributions -j^g-l^ 2. Predictive Intervals for Some LogStudent Predictive Distributions 2.92 IX

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LIST OF FIGURES Figure Page 1. Life Characteristics of Some Systems 21 AIII.l Predictive Distribution 186 AIII.2 Predictive Distribution ]87 AIII.3 Predictive Distribution 188 AITI.4 Predictive Distribution 139

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Abstract of Dissertation Presented to the Graduate Council of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy BAYESIAN MODELING OF NONSTATIONARITY IN NORMAL AND LOGNORMAL PROCESSES WITH APPLICATIONS IN CVP ANALYSIS AND LIFE TESTING MODELS By Jorge Ivan Velez-Arocho June 1978 Chairman: Christopher B. Barry Major Department: Management Probability models applied by decision makers in a wide variety of contents must be able to provide inferences under conditions of change. A stochastic process whose probabilistic properties change through time can be described as a nonstationary process. In tViis dissertation a model involving normal and lognormal processes is developed for handling a particular form of nonstationarity within a Bayesian framework. Two uncertainty conditions are considered; in one the location parameter, y , is assumed to be unknown and the spread parameter, a, is assumed to be known; and in the other both parameters are assumed to be unknown. Comparing the nonstationary model with the stationary one it is shown that: Â— 1. more uncertainty (of a particular definition) is present under nonstationarity than under stat ionarity ; 2. since the variance of a lognormal distribution, V(x) , is a function of \i and o"^ , nonstationarity in P means that both mean and variance of the random variable, x, are nonstationary so that the lognormal xi

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case provides a generalization of the normal results; and 3. as additional observations are collected uncertainty about stochastically-varying parameters is never entirely eliminated. The asymptotic behavior of the model has important implications for the decision maker. An implication of the stationary Bayesian model for normal and lognormal processes is that as additional observations are collected, parameter uncertainty is reduced and (in the limit) eliminated altogether. In contrast, for the nonstationary model considered in this dissertation the following inferential results are obtained: 1. for the case of lognormal or normal model, a particular form of stochastic parameter variation implies a treatment of data involving the use of all observations in a differential weighting scheme; and 2. random parameter variation produces important differences in the limiting behavior of the prior and predictive distributions since under nonstationnrity the limiting values of the parameters of the posterior and predictive distributions cannot be determined clearly. Practical implications of the results for the areas of CostVolume-Profit Analysis and life testing are discussed with emphasis on the predictive distribution for the outcome of a future observation from the data generating process. It is emphasized that a Cost-Volume-Profit (CVP) and life testing model ideally should include the changing character of the process by allowing for changes in the parametric description of the process through Lime. Failure to recognize nonstationarity when xii

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it is present has a number of imp! icnt ions in the CVP and life-testing contexts that are explored in tlie dissertation. For example, inferences are improperly obtained if the nonstationarity is ignored, and prediction interval coverage probabilities are overstated since uncertainty is greater (in a particular sense) when nonstationarity is present. .11

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CHAPTER ONE TNTRODUCTTON 1 . 1 Introduction Uncertainty is an essential and intrinsic part of the human condition. The opinions we express, the conclusions we reach and the decisions we make are often based on beliefs concerning the probability of uncertain events such as the result of an experiment, the future value of an investment or the number of units to be sold next year. If management, for instance, were certain about what circumstances would exist at a given time, the preparation of a forecast would be a trivial matter. Virtually all situations faced by management involve uncertainty, however, and judgments must be made and information must be gathered to reduce this uncertainty and its effects. One of the functions of applied mathematics is to provide information which may be used in making decisions or forming judgments about unknown quantities. Several early studies by econometricians and statisticians examined the problem of constructing a model. whose output is as close as possible to the observed data from the real system and which reflects all the uncertainty that tlie decision maker has. Mathematical models for statistical problems, for instance, have some element of uncertainty incorporated in the form of a probability measure. The model usually involves the formulation of a probability distribution of the uncertain quantities. This element of uncertainty is carried through

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2 the analysis to the inferences drawn. The equations that form the mathematica] model are usually specified to within a number of parameters or coefficients which must be estimated. The unknown parameters are usually assumed to be constant and the problem of model identification is reduced to one of constant parameter estimation. There are several reasons for suspecting that the parameters of many models constructed by engineers and econometricians are not constant but in fact time-varying. For instance, it has become increasingly clear that to assume that behavioral and technological relationships are stable over time is, in many cases, completly untenable on the basis of economic theory. Several recent studies provide support for the claim that the parameters of distributions of stock-price-related variables may change over time [see Barry and Winkler (1976)]. In engineering, particularly in reliability theory, the origins of parameter variation are usually not very hard to pinpoint. Component wear, variation in inputs or component failure are some very common reasons for parameter variations. The major objective of construction of engineering models is control and regulation of the real system modeled. Therefore, much of the research in that area has concentrated on devising ways to make the output of the model insensitive to parameter variation. Simil arly, in forecasting models for economic variables, researchers have had great concern with time varying parameters of the distributions of interest. In this area the problem of varying parameters has received increased attention because there is increasing evidence that the common regression assumption of stable

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liarametiirs often appears invalid. Ln tliis (I issertat inn we plan to study a particular type of random parameter variation whi.th is likely to be applicable when nonstat ionar ity over time is present. The modeling of nonstationarity that we are going to present assumes that successive values in time of the unknown parameter are related in a stochastic manner; i.e., the parameter variation includes a component which is a realization of some random process. For purposes of estimation we are interested in specific realizations of the random process. When the process generating the unknown parameter is a nonstationary process over time tlie decision maker should be concerned with a sequence of values of the parameter instead of a single value as in the usual stationary model; i.e., inferences and decisions concerning the parameter should reflect the fact that it is changing over time. If tlie values of an unknown parameter over time are related in a stochastic manner, a formal analysis of the situation requires some assumptions about the stochastic relationsliip . For the model of nonstationarity tliat we develop in this dissertation, the specification of the stochastic relationship between values of the parameter is sufficient. Moreover it is assumed that this relationship is stationary (usually referred to as second-order stat ionar ity) in the sense that the stochastic relat ionsliij) is the same for any pair of consecutive values of the unknovjn parameter. We \\'anr to gaii^i more precise information about tlie structure of the t iiiu'-vary ing parameters and to obtain estimated relationships

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that are suitable for forecasting. The model to be developed makes it possible to dra^j inferences about the structure of the relationship at every point in time. There are problems in accounting, life testing theory, finance and a variety of other areas that can benefit from nonstationary parameter estimation techniques. 1 . 2 Summ ary of Results and Ov e rvie w of Dissertation The goals of this dissertation are to develop a rigorous model for handling nonstationarity within a Bayesian framework, to compare inferences from stationary and nonstationary models, and to investigate inferential applications in the areas of Cost-Volxime-Prof it Analysis and life testing models involving nonstationarity. Probably the most important advantage of the new work to be presented in this dissertation is the Increased versatility it adds to the nonstationary Bayesian model derived by Winkler and Barry (1973). The new results enlarge the range of real and important problems involving univariate and multivariate nonstationary normal and lognormal processes which can be handled. Another advantage is the simplicity of the updating methods for the efficient handling of the estimation of unknown parameters and the prediction of the outcome of a future sample. A survey of the most relevant literature is provided in Chapter Two to set the stage for the new developments in the remainder of the dissertation. In tliis survey we present an overview of probabilistic CostVolume-Profit (CVP) Analysis and discuss the most important articles that deal with CVP under conditions of uncertainty. The review of the

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literature includes a section on life testing; models eniptiasizing the use of Bayesian tec.hnitiues used in life testing. It is empliaslzed that most of the research done in these two areas neglects the problem of nonstationarity. A special section Is presented to discuss some important articles about modeling nonstat ionary processes. As is mentioned in Chapter Two, most research concerned with the normal and lognormal distributions has considered cmly stationary situations. That is, the parameters and distributions used are assumed to remain tlie same in all periods. In Cliapter Tliree we develop a Bayesian model of nonstat ionari ty for normal and lognormal processes, In it we describe essential features of the Bayesian analysis of normal and lognormal processes under nonstat ionari ty , like the prior, posterior and predictive dist rihut icins . Two uncertainty conditions are considered in this chapter; in one the loc^ition parameter, y , is assumed to be unknovm and the spread parameter, a, is assumed to be known; and in the other, both parameters are assumed to be unknown. Comparing the nonstat ionary model with the stationary one It Is shown that: 1. more uncertainty (of a particular definition) is present under nonstat ionari ty than under stat lonarlty; 2. since the variance of a lognormal distribution, V(x), is a function of p and u^, nonstat iiinarity in p means that both mean and variance of the random variable, x, are nonstat ionary , so that the lognormal case provides a generalization of the normal results;

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6 and , 3. that, as adiiitiona] observations are collected, uncertainty about stochastically-varying parameters is never entirely eliminated. The results discussed in Chapter Three have to do with the periodto-period effects of random parameter variation upon the posterior and predictive distributions. However, the asymptotic behavior of tlie model has important implications for the decision maker. An implication of the stationary Bayesian model for normal and lognormal processes is that as additional observations are collected parameter uncertainty is reduced and (in the limit) eliminated altogether. Such an implication is inconsistent with observed real \;orld behavior largely because the conditions under which inferences are made typically change across time. The common dictiam [see Dickinson (1974)] has been to eliminate some observations in the case of changing parameters so that only those most recent observations are considered. In Chapter Four we show that: 1. for the case of a lognormal or normal model, a particular form of stochastic parameter variation implies a treatment of data involving the use of all observations in a differential weighting scheme, and, 2, random parameter variation produces important differences in the limiting behavior of the prior and predictive distributions since under nonstationar i ty the! limiting values of some of the parameters of the posterior and predictive distributions can not be determined clearly.

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One objecaive of tliis dissertation is to develop Bayesian prediction intervals for future observations that come from normal and lognormal data generating processes. In Chapter Four we address the problem of constructing prediction intervals for normal, Student, lognormal and logStudent distributions. It is pointed out tliat it is easy to construct these intervals for the normal and Student distributions but that it is rather difficult for the lognormal and logStudent distributions. An algorithm is presented to compute the Bayesian prediction intervals for the lognormal and logStudent distributions. Bayesian prediction intervals under nonstat ionar i ty are compared with classical, certainty equivalent and Bayesian stationary intervals. In Chapter Five we discuss the application of the results of Ciiapters Tliree and Four concerning nonstat ionarity to the area of CVP analysis and life testing models. Practical implications of our results for these two areas are discussed with emphasis on the predictive distribution for the outcome of a future observation from the data generating process. It is emj^hasized that CVP and life testing models ideally should include tlie clianging character of the process by allowing for changes in the parametric description of the process through time. It is shown that, for the case of normal ami lognormal data generating processes under a partii-ular form of stochastic parameter variation, the presence of nonstat ionar i ty produces greater uncertainty to tlie decision maker. Nonstat iona r i.ty implies greater uncertainty, whicli is reflected by an increase in the iiredictive variance of profits for CVP models,

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8 by an increase in tlie predictive variance of life length for life testing models, and by an increase in the width of intervals required to contain particular coverage probabilities. Chapter Six provides conclusions, limitations and suggestions for further research. Since stationarlty assumptions are often quite unrealistic; it is concluded in that chapter that the introduction of possible nonstationarity greatly increases the realism and applicability of statistical inference methods, in particular of Bayesian procedures.

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CHAPTER TWO SURVliY OF PERTINENT LITERATURE The primary purpose of the research in this dissertation is to present a Bayesian model of nonstationarity in normal and lo^normal processes witli ajipl ications in Cost-Volume-Profit analysis and life testing models. A survey of the most relevant literature is provided in the cliapter and will serve to set the stage for the new developments in the remainder of the thesis. In tills survey, three areas are covered. In Section 2.1 we present an overvlev\? of probal)ilistic Cost-Volume-Profit (CVP) analysis and discuss the most important articles that deal v;lth CVP under conditions of uncertainty. In Section 2.2 we discuss life testing models V\7ith an emphasis on the exi^onentlal , gamma, Weibull and lognormal models. The review of tlie literature includes a special section on Bayesian techniques used in life testing. Finally in Section 2.3 a survey is presented of some important articles about modeling nonstationary processes. 2 . 1 Cost -Volume-Profit (CVP)Analysi s Management requires realistic and accurate information to aid in decision making. Cost-Vo] unie-Prof i t (CVP) analysis is a widely accepted generator of information useful in decision making processes. CVP analysis essentially consists in examining the relationship between changes in volume ( output ) and changes in profit. The fundamental ass\imption in all types of CVP decisions is that the firm, or a department, or other Lvpe of costing unit, pt)ssesses a fixed set

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10 of resources tliat comniits tlie firm to a certain level of fixed costs for at least a slinrtrun periotl. I'he decision problem facing a manager is to determine the most efficient and productive use of this fixed set of resources relative to output levels and output mixes. The scope of CVP analysis ranges from determination of the optimal output level for a single-product department to the determination of optimal output mix of a large multi-product firm. All these decisions rely on simple relationships between changes in revenues and costs and changes in output levels or mixes. All CVP analyses are characterized by their emphasis on cost a\id revenue behavior over various ranges of output levels and mixes. The determination of the selling price of a product is a complex matter that is often affected by forces partially or entirely beyond the control of management. Nevertlieless, management must formulate pricing policies williin the bounds permitted by the market place. Accounting can play an important role in the development of policy by supplying management with special reports on the relative profitability of its various products, the probable effects of contemplated changes in selling price and otlier CVP relationships. The unit cost of producing a commodity is affected by such factors as the iniierent nature of tlie product, the efficiency of operations, and the volume of production. An increase in the quantity produced is ortlinaiily accompanied by a decrease in unit cost, provided tiie volume attained remains within tlie limits of plant capacity. Quantitative data relating to the effect on income of changes in

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11 unit selling price, sales volume, production volume, production costs, and operating expenses help management to improve the relationships among these variables. If a change in selling price appears to be desirable or, because of competitive pressure, unavoidable, the possible effect of the diange on sales volume and prod\ict cost needs to be considered. A mathematical expression of the profit equation of CVP analysis is: (2.1.1) Z = Q (P-V) F, where Z = total profits, Q = sales volume in units, 1' = unit selling price, V = unit variable cost, and F = total fixed costs. Tills accounting model of analysis has been traditionally used by the management accountant in profit planning. This use, howwver, typically ignores the uncertainty associated with the firm's operation, thus severely limiting its applicability. During the past 12 years, accountants have attempted to resolve this problem by introducing stochastic aspects into the analysis. The applicability of probabilistic models for this analysis has been claimed because of the realism of such models, i.e., decisions are always accompanied by uncertainty. Thus, the ideal model is one that gives a probability distribution of tlie criterion variable, profit, and that fully recognizes the uncertainty faced by the firm.

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12 The realism of sueli a nu)del is dependent on logical assumptions for the input variables and rigorous methodology in obtaining the output distribution. Further, we hope that, the model can accomodate a wide range of uses. For example, the capability to handle dependence among input variables adds a highly useful dimension. Jaedicke and Robichek (196A) first introduced risk into the model. They assum.ed the follovying relation among the means (2.1.2) F(Z) = E(Q) [E(P) E(V)1 E(F) , where E( Â•) denotes mathematical expectation. In addition they assumed that the key variables were all normally distributed and tliat the resulting profit is also normally distributed. Thus, by computing the r^iean value and standard deviation of the resulting profit function, various probabilistic measures of profit can be obtained. This model has been depicted as a limit analysis, since the assumptions of the independent model parameters and tlie normalcy of the resulting profit fimction are not true except in limiting cases. According to Ferrara, Hayya and Nachman (1972), the product of two normally and independently distributed variables will approximate normality if the sum of the two coefficients of variation is less than or equal to .12. Others have confronted the same problem of how to identify the resulting profit distribution when it is not close to a normal distribution. They have noted that it is often difficult to obtain analytical, expressions for the product of random variables. Because the ap[)ropiate d i :: t ri buL i onal forms for tlie product of the variable

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13 tunc I: ions may not In? known, Bu/.by (1974) suggests the application of Tchebychef f ' s theorem to stochastic Cost-Volume-Profit analysis. This theorem, ho\v;ever, permits the analyst to derive only some very crude bounds on the probabilities of interest, so its value as a decisionmaking tool is limited. Liao (1975) illustrated liow m.odel sampling (also called distribution sampling) coupled with a curve-fitting technique can be used to overcome the above problems associated with stochastic CVP analysis. In his paper, the illustration of the proposed approach to stochastic CVP analysis is first developed through a consideration to the Jaedicke-Robicheck problem, wherein the model parameters are independent and normally distributed. After that, the Illustration problem is modified to accomodate dependent and non-normal variates in the problem. Milliard and Leitch (1975) developed a model for CVP analysis assuming a more tractable distribution for the inputs of the equation. It allows fur dependent relationships and permits a rigorous derivation of the distribution of profit. The problems of assuming price and quantity to be independent are pointed out. The authors also pointed out that assuming sales to be normally distributed implies a positive probability of negative sales. Probabilities and tolerance intervals for the Hilliard and Leitch model are obtained from tables of the normal distribution. The only assumptions i-equired for the model are (1) quantity and contr il)ut ion margin are lognormally distributed random variables and (2) fixed costs are deterministic. The assumption that sales

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14 quantity and contribution margin are bivariate loRnormally distributed eliminates the possibility of negative sales and of selling prices below variable costs, and it has the nice additional property that the product of two bivariate iognormal random variables is also lognormal. Thus, \^Ie can allow for uncertainty in price and quantity and still have a closed form expression for the probability distribution of gross profits. Hilliard and Leitch can not assume that price and varial)le costs are marginally lognormally distributed and have contribution margin also be lognormally distributed. Similarly, if fixed costs are assumed to be lognormally distributed too, net profits will not be lognormally distributed. Adar, Barnea and Lev (1977) presented a model for CVI^ analysis under uncertainty tiiat combines the probability characteristics of the environment variables with the risk preferences of decision makers. The approach is based on recently suggested economic models of the firm's optimal output decision under uncertainty, which were modified within tlu' mean-standard deviation framework to provide for a cost-volume-ut ii i ty analysis allov.'ing management to: (1) determine optimal output, (2) consider the desirability of alternative plans involving changes in fixed and variable costs, expected price and uncertainty of price and technology changes and (3) determine the economic consequences of fixed cost variances. Dickinson (1974) addresses tlie problem of CVP analysis under uncertainty by exaniining the relialiility of using the usual methods of estimating the means .ind variances of the past distributions of

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15 sales demand, lie emphasized tliat, wliea the expectation and variance of profits are estimated from past data, it is important to differentiate between what, in fact, are estimated and what are true values of the parameters. In other words, he pointed out that the estimated expectation of profits, I'Uti ) , reflects estimation risk and is not equal to E(n ) . Classical confidence intervals v^?ere used for tlie expected value of profits, E(-n) , for the variance of profits, Var(7r)i and for probabilities of various profit levels. However, Dickinson misinterpreted the classical confidence intervals that he obtains in his paper. When a classicist constructs a 90 percent confidence interval for ]s, for example, he would state that in the long run, 90 percent of all such intervals v%?i 1 1 contain the true value of p. Tlie classical statement is based on long-run frequency considerations. The classicist is absolutely opposed to the interpretation that tlie 90 percent refers to the probability tliat tlie true universe mean lies within the specified interval. In the eyes of a classicist, a unique true value exists for the universe mean, and therefore the value of the universe mean cannot be treated as a random variable. Dickinson's paper also illustrates the difficulty of obtaining the probability statements of greatest interest to management in a classical approach. His analysis is only able to provide confidence intervals of probabilities of profit levels rather tlian the profit level probabilities themselves. The probliMii of parameter uncertainty has been neglected by the people that have studied CVP analysis under uncertainty. In the liayesi
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16 models is reflected in prior and posterior probability statements regarding the parameters. Marginal distributions of variables which depend on tliose |iarameters may be obtained by integrating out the distribution of the parameters, thereby obtaining predictive distributions [see Roberts (1965) and Zellner (1971)] of the quantities of interest to the manager. These predictive distributions permit one to make valid proliability statements regarding the important quantities, such as profits. Nonstat ionarity is another important aspect related to CVP analysis that no one has considered. In a world that is continually changing, it is important to recognize that the parameters that describe a process at a particular point in time may not do so at a later point in time. In the case of the variable siiles, for instance, experience shows that it is typically affected by a variety of economic and political events. Thus, a CVP model ideally should include the changing character of the process by allowing for changes in the parametric description of the process through time. Failure to recognize the nonstationary conditions may result in misleading inferences. lii this dissertation the problem ofCost-Volume-Profit analysis Vv/ill be considered from a Bayesian viewpoint, and inferences under a special case of nonstationarity will be considered. Also the Bayesian results under nonstationarity will be compared with tliose results that can be obtained under a stationary Bayesian model, and the Bayesian model will 1m> c:ompared with some alternative approaches.

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17 2.2 Life Testing Models 2.2.1 Introduction The development of recent technology has given special importance to several problems concerning the improvement of the effectiveness of devices of various kinds. It is often important to impose extraordinarily high standards on tlie performance of these devices, since a failure in the performance could bring disastrous consequences. The quality of production plays an important role in today's life. An interruption in the operation of a regulating device can lead not only to deterioration in the quality of a manufactured product but also to damage of the industrial process. From a purely economic viewpoint high reliability is desirable to reduce costs. However, since it is costly to achieve high reliability, there is a tradeoff. The failure of a part or conijionent results not only in the loss of the failed item but often results in the loss (at least temporarily) of some larger assembly or system of which it is part. There are numerous examples in wiiioh failures of components have caused losses of millions of dollars and personal losses. The space program is an excellent example where even the lives of some astronauts were lost due to failure in the system. The follo^^;ing authors have considered the statistical theory of reliability and provide a good set of references on the subject: Mendenhall (1958), Buckland (1960), Birnbaum (1962), Covind.ira i II 1 u (IMtjA), Mann, Scliaefer and Singpurwaila (1973), and Canfield and liorgiiian (1975). Ki_' 1 iabil i I y tlieory is the disc ii^l inc' tliat deals with procediires

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18 to ensure tlie maximum effectiveness of manufactured articles and tliat develops methods of evaluating the quality of systems from knovm cjualities of their component parts. A large num.ber of problems in reliability theory have a mathematical character and require the use of mathematical tools and the development of new ones for their solution. Areas like probability theory and mathematical statistics are necessary to solve some of the problems found in reliability theory. No matter how liard the company works to maintain constant conditions during a production process, fluctuations in the production factors lead to a significant variation in the properties of the finished products. In add ition, articles are subjected to different conditions in the course of tlieir use. To maintain and to increase the reliability of a system or of an article requires both material expenditures and scientific research. Statistical tlieory and methodology have played an influential role in the development of reliability theory since the publication of the paper by Epstein and Sobel (1953). Four statistical concepts provide the basis for estimating relevant parameters and testing hypotheses about the life characteristic of the subject matter. These concepts are: (1) the distribution function of some variable which is a direct or indirect measure of the response (life time) to usage in a particular euvirnnment; (ii) tlie associated probability density (or frequency) function :

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19 (ill) the survival probability function; and (iv) the conditional failure rate. A failure distribution provides a mathematical description of the length of life of a device, structure or material. Consider a piece of equipment which has been in a given environment, e. Tlie fatigue life of this piece of equipment is defined to be the length of time, T(e), this piece of equipment operates before it fails. Full information about e would fully determine T(6) , so that given e, T(e) would not be random. One source of randomness in life is in uncertainty about the environment, i.e., T(e) is a random variable because e is random. Equipment has different survival characteristics depending nn the conditions under which it is operated, and e provides a statement of what conditions are but does not determine T(e) fully. The reliability of an operating system is defined as the probability that the system will perform satisfactorily within specified conditions over a given future time period when the system starts operating at some time origin. Different distributions can be distinguished according to their failure rate function, which is known in the literature of reliability as a hazard rate [see Barlow and Prosch.m (1965) ] . The hazard rate (denoted by h) , which is a function of time, gives the conditional density of failure at time, t, wit]i the hypothesis that the unit has been funcitoning without failure up to that point in time. The conditional failure is defined as: (2.2.1) h(t) = f(t)/[L F(t)] = f(t)/R(t) ,

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20 where (2.2.2) F(t) = Prob (T < t) = f^ f(t) ds , is the probability that an observed value of T will be less than or equal to an assigned number t. The reliability function (also called the survival function) of the random variable T gives the probability that T will exceed t and is defined by (2.2.3) R(t) = 1 F(t) = Prob (T > t) . The probability density function of the random variable T, f(t), < t < oo, is knovm as the failure density function of the device. It can be shown that the conditional failure rate and the distribution function of a random variable are related by (2.2.4) F(t) = 1 exp[f^ h(s) d(s)]. The causes of failure can be categorized into three basic types. It is recognized, however, that there may be more than one contributing cause to a particular failure and that, in some cases, there may be no completely clearcut distinction between some of the causes. The three classes of failure are infant mortalities, or early failures, random failures and wearout failures. The behavior of the hazard rate as a funciton of time is sometimes known as the hazard function or life characteristic of the system. For a typical system that may experience any of the three previously described types of failure, the life characteristic will appear as in Figure 1. The representation of the life characteristic has been classically referred to as the "bathtub curve", wherein the three segments of the curve represent the three time periods of initial, chance and wearout failure,

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21 Time Figure 1. Life characteristics of some systems The initial failure period is characterized by a high hazard rate shortly after time x=0 and a gradual reduction during the initial period of operation. During the chance failure period, the hazard rate is constant and generally lower than during the initial period. The cause of this failure is attributed to unusual and unpredictable environmental conditions occuring during the operating time of the system or of the device. The hazard rate increases during the wearout period. This failure is associated with the gradual depletion of a material or an accumulation of shocks and so on. In the following subsections ue will consider the general

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22 properties of some widely used life distributions, the assessment and use of those distributions, and the literature related to Bayesian methods in life testing. 2.2.2 Some Common Life Distr i butions J . 2 . 2 . 1 The Expone n tial Distribution In the case of a constant failure rate the distribution of life is exponential. This case has received the most emphasis in the literature, since, in spite of theoretical limitations, it presents attractive statistical properties and is highly tractable. Data arising from life tests under laboratory or service conditions are often found to conform to the exponential distribution. An acceptable justification for the assumption of an exponential distribution to life studies was initially presented by Davis (1952). More recently Barlow and Proschan (1965) have advanced a mathematical argument to support the plausability of the exponential distribution as the failure law of complex equipment. The random variable T has an exponential distribution if it has a probability density function of the form (2.2.5) tj,(t) = o"^ exp[-(t-0)/o] , t > 6, o > 0. The mean and variance of T are (o + 9) and a^ , respectively. In most a])pllcat ions is taken as zero. For this distribution, the physical interpretation of a constant hazard function is that, irrespective of the time elapsed since the start of operation, of a system the probability that the system fail in the next time intervals dt.

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23 given that it ha.s survived to time t, is independent of the elapsed time t and is constant. 2.2.2.2 The Gamma Distribution An extremely useful distribution in fatigue and wearout studies is the gamma distribution. It also has a very important relationship to the exponential distribution, namely, that the sum of n independent and identically distributed (i.i.d.) exponential random variables with common parameters Q=0 and a is a random variable that has a gamma distribution with parameters n and o. Hence, the exponential distribution is a special case of the gamma with n=l. Tiie random variable T has a gamma distribution if its probability density function is of the form, (2.2.6) f (t) = t(t-9)"""^ exp[-(t-6)/al} /a'Y(n); n > 0, a > 0, e > 0. The standard form of the distribtuion is obtained by puttiiig o = l and 6=0, giving (2.2.7) f^(t) = [t"~^ exp(-t)]/r(n), t>0; where the gamma function, denoted F, is a mapping of the interval (0,Â°Â°) into itself and is defined by (2.2.8) r(n) = / t"~^ exp(-t) dt. The probability distribution function of (2.2.7) is (2.2.9) ProbiT < t] = [r(n)]-l f^ x""^ exp(-x) dx .

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24 Since a distribution of the form given in equation (2,2.6) can be obtained from standardized distributions, as in equation (2.2.7), by the linear transformation t=(t'-e)/o, there is no difficulty in deriving formulas for moments, generating functions, etc., for equation (2.2.6) from those for equation (2.2.7). One of the most important properties of the distribution is the reproductive property; if T, and T are independent random variables each having a distribution of the form (2.2.7), possibly with different values n', n" of n but \jith common values of a and 0, then (Ti+ T2) also has a distribution of this form, with the same value of o and 0, and with n = n' + n" . 2.2.2.3 The Meibull Distribution The WeibuU distribution was developed by W. Weibull (1951) of Sweden and used for problems involving fatigue lives of materials. Three parameters are required to uniquely define a particular Weibull distribution. Those three parameters are the scale parameter a, the shape parameter n and the location parameter G. A random variable T has a Weibull distribution if there are values of the parameters n (>0) , a (>0) and such that, (2.2.10) Y = [(t-0)/a]" has the exponential distribution with probability density function (2.2.11) f^,(y) = exp(-y), y > 0. The probability density function of T is given by

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26 at succesive points of time of, for example, a fatigue crack or the growtli of biological organisms and the change between any pairs of succesive steps or stages is a random proportion of the previous size, then asymptotically the distribution of the random variable is lognormal [see Kapteyn (1903)1. This theoretical result imparted some plausibility to the lognorraal distribution for failure problems. Let t, < tÂ„< ... < t be a sequence of random variables that denote the -1-2 n ' sizes of a fatigue crack at succesive stages of its growth. It is assumed that the crack growth at stage i, t.t._,, is randomly proportional to the size of the crack, t . _-. and that the item fails v>7hen the crack reaches t . Let t_j^t_^_-, = it . t . _.. , i= 1 , 2 , . . . , n, where TT . is a random variable. The n . are assumed to be independently distributed random variables tliat need not have a common distribution for all i's when ii is large but that need to be lognormally distributed otherwise. 'ITius, TTi = (t.^L-p/'^i-l ' i = 1, 2, ... , n . Mann, Schaefer and Slngpurwalla (1973) show tliat In t , the life length of the item, for large n, is asymptotically normally distributed, and hence t has a lognormal distribution. If there is a number y such that (2.2.18) Z = In(t-Y) is normally di st r i liuLcd , then the distribution of t is said to be lognormal. The d i si r i but i cm of t can be defined by the equation, (2.2.19) U = -/, + 6 In(t-y) ,

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25 (2.2.12) f^(t) = no"^ [(t-e)/a]"~^ exp{ [ (t-O) /a] } " , t > 6. Tlie standard WeihuU distribution is obtained by putting a = l and 6=0. The value zero for is by far the most frequently used, especially in representing distributions of life times. The Weibull distribution has cumulative distribution function (2.2.13) F,|,(t) = l-exp{-[(t-e)/a]"} , and its mean and variance are (2.2.14) E(t) = ar(l + H/n]) and (2.2.15) Var(t) = o^i r(l+[ 2/n] ) r2(H-[l/n])} , respectively. For the two parameter Weibull distribution we have that the reliability and hazard function are (2.2.16) R.jXO = exp [-(t/o)"l and (2.2.17) h^,(t) = nl"~Vo" . Wien n=l, the hazard function is a constant. Thus the exponential distribution is a special case of the Weibull distribution v^;ith n=l. 2.2.2.4 The Lognormal Distribution The lognormal distribution is also a very popular distribution in describing wearout failures. This model was developed as a physical or, more appropiately biological, model associated with the theory of {iroport ionate effects (see Aitchison and Brown (1937) for a full description of the distribution, its properties, and its developments). Briefly, if a random variable is supposed to represent the magnitudes

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27 where U is n unit noriiKil variable and 0, fi and y are parameters. The probability den.sitv function of T is defined by (2.2.20) f^,(t) = 5[(t-Y)/2^|"^ exp[-{Si+Sln(t-Y)}"/2], t>v An alternative, more fashionable notation replaces Q and 6 by the expected value m and standard deviation a of Z = In(t-Y). The two sets of parameters are related by the equations, (2.2.21) p = -il/6 and (2.2.22) o = 6~^ so that the distrilnitlon of t can be defined by (2.2.23) U = fln(t-Y) p]/a and the probability density function of T by (2.2.24) f^(t) = [(t-Y)>^a]-l exp [-{ ln( t-y) -y }2 /2a2 ] , t>Y . In many applications, y is known (or assumed) Lo be zero. This iminirtant case has been given the name two parameter lognormal distribution. The mean and variance of the two parameter distribution are given by (2.2.25) mt) = exp[y + (0^/2)] , and (2.2.26) Var(i) = [exp(2p)]
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28 In addition, the value t such that Fr(t
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29 2.2.3 T raditional Approach t o Life Testing Inferences In life testing theory we find a large niimher of random quantities. In most cases we do not know the distributions and theoretical characteristics; our aim is to estimate some of these quantities. This is usually accomplished with the aid of observations on the random variables. According to the laws of large numbers, an "exact" determination of a probability, an expected value, etc., would require an "infinite" number of observations. Having samjales of finite size, we can do no more than estimate the theoretical values in question. The sample characteristics, or statistics, serve the purpose of statistical estimation. For a good estimation of theoretical quantities, a fairly large sample is sometimes needed. In many practical situations the following two types of estimation problems arise. A certain quantity, say t) , which is, from the statistical point of view, a theoretical quantity, lias to be determined by means of measurement. Such a quantity may be, for example, the electrical resistance of a given device, the life of a given product, etc. The result T of the measuring procedure is a random variable whose distribution depends on 9 and perhaps on additional quantities. That is, we have to estimate the parameter 9 out of a sample T, , T,, , ... , T taken on T. In the J2 n other case, the quantity in question is a random variable itself and in such cases we are interested in tlie (theoretical) average value, or the dispersion of 1', etc. This means that we have to estimate the expected value E(T) or Var(l'), and perhaps other (constant) quantities that caii be expressed with the aid of the distribution

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30 function of T, like the reliability function. More often for lifetime distributions, tlie quantity of interest is a distribution percentile, also knovvn as tlie reliable life of the item to be tested, corresponding to some specified population survival proportion; or it is the population proportion surviving at least a specified time, say S , For the classical statistician, the unknown parameter 9 is considered to be a constant. In estimating a constant valtie there are various aspects to consider. If we wish to have an estimator whose value can be used instead of the unknown parameter in formulas [certainty equivalent (CE) approach], then tlie estimator should have one given value. In this case we speak of point estimation. Rut knowing that our estimator is subject to error, sometimes we would like to have some information on tlie average deviation from the value. In this case we have to construct an interval that contains the unknovm parameter, at least with high probability, or give a measure of the variability of the estimator (such as the standard error of the estimate) . ^k5st of the literature about the traditional approach to life testing inferences is focused in two areas; one relates to point and interval estimation procedures for lifetime distributions and the other relates to methods of testing statistical hypotheses in reliability (known as "reliability demonstration tests") . The classical, approach to point estimation in life testing inferences emptiasizes that a good estimator should have properties like unbiasedness , efficiency, consistency and sufficiency [see

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31 ly Dubey (1968), BartletC (1937) and Weiss (1961)]. Two methods, tlie method of niomeats and iiu'thod of maximum likelihood, are frequentl; used to yield estimators with as many as possible of the previously mentioned properties. Under various sampling assumptions, the maximum likeliliood estimators of the parameters Vv^ere obtained for the following distributions; gamma [see Choi and Wette (1969) and Harter and ^k^ore (1965)); Weihull [see Bain (1972), Bil Imaii e^ j_l Â• n971), Cohen (1965), Englehardt (1975), Haan and Beer (1967), Lemon (1975) and Rockette e t a 1 . (1973)]; exponential [see Deemer and Votaw (1955), El-Sayyad (1967) and Epstein (1957)]; and for the normal and lognormal [see Cohen (1951), Harter and Moore (1966), Lambert (1964) and Tallis and Young (1962)]. The traditional approach also includes some linear estimation properties like Best Linear Unbiased (BLU) and Best Linear Invariance (BLI) . Interval estimation procedures have also been developed for the parameters of the life distributions. Examples include Bain and Englehardt (1973), Epstein (1961), Harter (1964) and Mann (1968). Point or interval estimators for functions of the life distributions, such as reliable life, reliability function, hazard rate, etc. , were obtained by substituting for the unknov^'n parameters the point or interval estimators obtained for them [see Johns and Lieberman (1966), Bartholomew (1963), Criibbs (1971), Harris and Singpurwalla (1968, 1969), Lawless ( J 9 7 1 , 1 9 7 2) , Likcs (1967), Mann (19h9-a, 1969-b, 1970), Varde (1969) and Linliai t (1M(,-,) j .

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32 Testing reliability liypotheses is the second major area of research in the classical approach to life testing. By means of the methods referenced previously, a test statistic is selected, regions of acceptance and rejection are set up, and risks of incorrect decisions are calculated. In addition it is emphasized that the risks of incorrect decisions are specified before the sample is obtained, and in tiiis case n, the sample size, is generally to be determined. Some of the references in this area include [Epstein (1960), Mpstein and Sobel (1955), Kumar and Patel (1971), Lilliefors (1967, 1969), Sobel and Tlschendorf (1959), Thoman et al . (1969, 1970) and Ferclio and Ringer (1972)]. A large part of the statistical problem in reliability involves the estimation of parameters in failure models. Each of the methods of obtaining point estimates previously referenced has certain statistical properties that make it desirable, at least from a theoretical viewpoint. Not surprisingly, point estimates are often made (particularly in reliability) because decisions are to be based on them. The consequences of the decisions based on the estimates often involve money, or, more generally, some form of utility. Hence the decision maker is more interested in the practical consequence of the estimate than in its tlieoretical properties. In particular, he may be interested in making estimates that minimize the expected loss (cost), but this can not be accomplished in general with classical methodology because the methodology does not admit probaliility distributions of the parameters.

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33 2.2.4 B ayesian Techniques in Life Testing Tlie non-liayeslan (classical) approach to estimation considers an unknown parameter as fixed. This means that classical interval estimation and liypothesis testing must lean on inductive reasoning either through the likelihood function or the sampling distributions. Tn point estimation, the classical approai^h must depend on estimates the criteria for which often are not based on the practical consequences of the estimates. On the other hand, Bayes procedures assume a prior distribution of the parameter space, that is, considers the i^arameter as a random variable, and, hence, tlie posterior distribution is available. This creates the possibility of a whole new class of criteria for estimation, namely, minimization of expected loss, probability intervals and others. In view of the difficulty in assessing utility or costs of complex reliability prol^iems, in previous studies Bayesian methods have been used primarily to provide a means of combining previous data (expressed as the prior distribution) with observed data (expressed in the likelihood function) to obtain estimates of parameters by using the posterior density. However, it must be emphasized that Bayesian methods are perfectly general in providing whatever the reliability problem demands. Tliere is a loss function that is rather popular in Bayesian analysis anil gives simple results. Su()pose that 6 is an estimate of i) and that the loss function is (2.2.28) L(u,u) = (e-e)-^.

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34 This function states that the loss is equal to the square of the distance of 9 from 0. The Bayes approach is to select the estimate of that minimizes the expected loss witli respect to tlie posterior distribution. The estimate that accomplishes this is the posterior mean, that is, (2.2.29) 6 = E(0| t^, t2, ... , t^;P) ; where P represents prior information. The above loss function is often called the quadratic ]oss function and the posterior mean is termed the Bayes estimate, if the loss function is of the form (2.2.30) L(e,6) = |6-e| , the estimate of 6 that minimizes the expected loss is the median of the posterior distribution. Canfield (1970) developed a Bayesian estimate of reliability for the exponential case using this loss function. The resulting estimate is seen to be the MVUE of reliability when the prior is flat. A third and simple case is the asymmetric linear, (2.2.31) 1.(0,0) = ky (0-6) if ex k (e-9) . if e
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35 The expected loss is gmierally a random variable a priori since it depends on the as yet unobserved sample data. The unconditional expectation (witli respect to tlie sample) of the expected loss is called the "Bayes risk" and is minimized by the Bayes estimate. Hayes methods liave been used in a variety of areas of reliability. Most uses can be characterized as point or interval estimation of parameters of life distributions or of reliability functions. Examples include Breijiohl, et. al., (1965) who studied the behavior of a family of Bayesian posterior distributions. In addition the properties of the mean of the posterior distribution as a point estimate and a method for constructing confidence intervals were given. The problem of hypothesis testing was considered, among others, by MacFarland (1972). IKprovided a simple exposition of the rudiments of applying Bayes equation to hypotlieses concerning reliability. The Bayesian approach has also been applied to parameter estimation and reliability estimation of some known distributions like gamma, PoissiMi, lognormal and others. Lwin and Singh (1974) considered a Bayesian analysis of a two-parameter gamma model in life testing context with special emphasis on estimation of the reliability function. Tlie Poisson distribution has received the attention of Canavos (1972, 1973). In the first article a smooth empirical Bayes estimator is derived for the hazard rate. The reliability function is also estimated either by using the empirical Bayes estimate of the |i,irameters , or by obtaining the expectation

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36 of the reliability function. Results indicate a significant reduction In mean squared error of the empirical Bayes estimates over the maximum likelihood estimates. A similar result v^as also derived for the exponential distribution by hemon (1972) and by Martz (1975). Next, Canavos developed Bayesian procedures for life testing witli respect to a random intensity parameter. Bayes estimators were derived for the Poisson parameters and reliability function based on uniform and gamma prior distributions. Again, as expected, the Bayes estimators have mean squared errors (MSE) that are appreciably smaller than thoseof the minimum variance unbiased estimator (MVUE) and of tlie maximum likelihood estimator (MLE) . Zellner (1971) has studied the Bayesian estimation of the parameters of the lognormal distribution. Employing a flat prior, Zellner found that the MSE estimators of the parameters are the optimal Bayesian estimators when a relative squared error loss function is used. The Weibull and exponential function have received most of the attention of authors who have studied life distributions from a Bayesian viev^7point. The Weibull process with unknown scale parameter is taken as a model by Soland (1968) for Bayesian decision theory. The family of natural conjugate prior distributions for the scale parameter is used in prior and posterior analysis, in addition, prepostorior analysis is given for an acceptance sampling problem with utility linear in the unknown mean of the Weibull process. Soland (1969) extended the analysis by treating both the shape and scale

PAGE 50

37 parameters as uiikmnvn, but as was previously kuovvai Jt is not possible to find a family of continuous ^iiint distributions on the two parameters that is (.-Itjsed under sampling, so a family of prior distributions is used that places continuous distributions on the scale parameter and discrete distributions on tl\e shape parameter. Prior and posterior analysis are examined and seen to be no more difficult than for the case in which only the scale parameter is treated as unknov\;n , but posterior analysis and determination of optimal sampling plans are considerably more complicated in this case. In Bury (1972), a two-parameter Welbull distribution is assumed to be an appropiate statistical life model. A Bayesian decision model is constructed around a conjugate probability density function for the Weibull hazard rate. Since a single sufficient statistic of fixed dimensionality does not exist for the Weibull model, Bury was able to consider only two sampling plans in his preposterior analysis: obtain one further observation or terminate testing. Bury points out that small sample Bayesian analysis tends to be more accurate than classical analysis because of the additional prior information utilized , in the analysis. Bayes credible bounds for the scale parameter and for the reliability function are derived by Papadopoulos and Tsokos (1975). Reliability data often include information that the failure event lias not yet occuL-red for some items, while observations of complete lifetimes are available for other items. Cozzolino (197A) addressed this problem from a Bayesian point of view, considering

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38 density functions that \\a.\e failure rate functions consisting of a known function multiplied by an unknown scale factor. It is shov-zn that a gamma family of priors is conjugate for the unknown scale parameter for both complete and incomplete experiments. A very flexible and convenient model resulting from the assumption of a piecewise constant failure function. Life tests that are terminated at preassigned time points or after a preassigned number of failures are sometimes found in reliability tlieory. Bhattacharya (1967) provided a Bayesian analysis of the exponential model based on this kind of life test. He showed that the reliability estimate for a diffuse prior (wliich is uniform over the entire positive line) closely resembles the classical MVUE, and he considered the role of prior quasi-densities when a life tester has no prior information. Bliattacharya points out that the use of constant density over the positive real line has been suggested to express ignorance but that it causes problems. For example it can not be interpreted as a probability density since it assigns infinite measure on the parameter space. [See Box and Tiao (1972).] A paper by Dunsmore (1974), stands out from among the other Bayesian papers in life testing and is particularly pertinent to the life testing application in this thesis. This article is an important exception because it carries the Bayesian approach to its natural conclusions by determining prediction intervals for future If g(0) is any non-negative function defined in the parameter space U such that g(r;) f (J V e .Q , then g(b) is called a prior quasi-densi ty .

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39 observations in life testing nsinj^ the concept of the Bayesian predictive distribut iiin . One objective of prediction is to provide some estimate either point or interval, for future observations of an experiment F based on the results obtained from an informative experiment E. As we mentioned before, the classical approach to prediction involves the use of tolerance regions. [See Aitchison (1966), Folks and Broi%me (1975), Guenther et al. (1976) and Hewett and Moeschberger (1976)]. In these we obtain a prediction interval only, and the measure of confidence refers to the repetitions of the whole experimental situation. The Bayesian approach on the other hand, allows us to incorporate further information which might be available through a prior distribution and leads to a more natural interpretation. Let t , ..., t be a random sample from a distribution with probability density function i'(t|6), (tcT;0eO), and let y,,, y.,, ..., y be a second independent random sample of "future" observations from a distribution witli probability density function F(y|o), (yeY;Gc0). Our aim is to make predictions about some function of y , y^, ..., y . The Bayesian approach assumes that a prior density function P(6), (8t:G) is available that measures our uncertainty about the value of 0. If the information in E is summarized by a sufficient statistic t then a posterior distribution P(6|t) is available. Suppose now that we wish to predict some statistic y defined on y , y,,, ... y . Then Such a sufficient statistic will always exist since, for T example, t could [
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40 the predictive density function for y is given by (2.2.32) P(ylt) = / P(y|0) P(6|t) de \ A Bayesian prediction interval of cover g is then defined as an interval I such that , (2.2.33) P(l|t) = / P(y|t) dy = 6. [See, for example, Aitchison and Sculthorpe (1965), Aitchison (1966) and Guttman (1970).] It should be emphasized that in the Bayesian approach the complete Inferential statement about y is given by the predictive density function P(y|t). Any prediction interval is only a summary of the full description P(y|t). In general there will be many intervals I that satisfy (2.2.33). Dunsmore considers most plausible Bayesian prediction intervals (conmionly known as highest posterior density (HPD) intervals) of cover (3, wliich have the form, (2.2.34) I = [y:P(y |t) > X] , where A is. determined by P(I|t) = 3. In conclusion we might say that the "uses of Bayesian methods in life testing have been limited. However in those cases where Bayes estimators have been found, they performed better, according to classical criteria, tlian the conventional ones. The use of loss functions has not been analyzed deeply for the reasons mentioned before; namely It is implicitly assumed in (2.2.32) that conditional on , y and t are independent.

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that the loss funt'tion is usually complex and unknown, and that even when tlie hiss 1; miction is known the Bayes estimate is sometimes difficult to find. Some of these problems wi i,l be solved with the development of mathematical theory and probably with the development of computer systems. Only the Dunsmore paper fully used the Bayesian methodology to obtain prediction Intervals that consider all available information and fully recognize the remaining parameter uncertainty. All of the papers discussed in the previous section considered a stationary situation. That is, the known parameters and the distributions used are assumed to remain the same across all time periods. It would be of value to study the nonstationary case, where tiie parameters are changing in time and possibly tlie distributions could also change in time. It is important to recognize, however, that probably the |iroblems now faced ^^/itll the stationarity assumption will be greater when tliat assumption is relaxed. Never this dynamic system is well worth investigating. 2 . 'i Modeling of Nonstationary Processes For many real world data generating processes the assumption of stationarity is questionable. Take for instance life testing models. l\nien it is assumed tiiat the life of certain commodities follows a lognorm.al distribution, for example, the stationarity assumption could be expecti'd to hold over short periods of time; but in most cases it \Jould be expected tliat for a lengtliy period, stationarity would hi a doubtful assumjJt ion . If the model represents the life of perishable |)rodut:ts, like food for example, then it

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42 v%fOu]d be expected that enYircnmental factors like heat and humidity could change and affect the cliaracteristics of the life distribution of the product or affect the input factors used in the manufacturing process. Furthermore, the wearout of the machines used in the manufacture of the products could cause changes in the quality of the products and hence in the parameters of the life distributions. Random parameter variation is surely to be a reasonable assumption when we are concerned with economic variables, like those used in Cost-Voluiue-Prof it analysis. A wide spectrum of circumstances could be mentioned where the economic environment is gradually affected. For exam[)le, the level of economic development changes gradually in a country and consequently brings gradual changes in related variables like income, consumption and price. Also, consumer's tastes and preferences evolve relatively slowly as social and economic conditions change and as new marketing channels or techniques are developed. The gradual increase in technology available to the industry and to the government may produce changes that are not dramatic but that will liave some Influence in any particular period of time. In other words, it seems reasonable to assume that in at least some situations the distribution functions of variables, like sales, price or costs, could be gradually changing in time. It is important to emphasize that we are referring to gradual changes, the effects of which are not perf(^ctly predictable in advance for a particular period. If a data generating process characterized by some parameter e is nonstat ionary , then it is not particularly realistic to make

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43 inferences and decisions concerning 6 as if only took on a single value. Instead we should be concerned with a sequence 6,, eÂ„, ... , of values of 6 corresponding to different time periods, assuming the characteristics of the process vary across time but are relatively constant within a given period. Some researchers have studied this problem with particular stochastic processes. Chernoff and Zacks (196A) studied what they called a "tracking" problem. Observations are taken on the successive positions of an object traveling on a path, and it is desired to estimate its current position. If the path is smooth, regression estimates seem appropiate. Hov;ever, if the path is subjected to occasional changes in direction, regression will give misleading results. Their objective was to arrive at a simple formula which implicitly accounts for possible changes in direction and discounts observations taken before the latest change. Successive observations were assumed to be taken on n independently and normally distributed random variables v^ith means y-, , p-,, ... , (i . JÂ»n Each mean is equal to the preceding mean except vjhen an occasional change takes place. The object is to estimate the current mean p Â• They studied the problem from a Bayesian point of view and made the following assumptions: tlie time points of change obey an arbitrary specified a priori probability distribution; the amounts of change in the means (when changes take place) are independently and normally distributed random variables with zero mean; and the current mean U is a normally distributed random variable with zero mean. Using a quadratic loss fimctiou and a uniform prior distribution for y-. on

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44 the wliole real line Lhey derived a Bayes estimator of \i . In addition they derived llie minimum variance linear unbiased (MVLU) estimator of y . Comparing both estimators they found tliat although the MVLU estimator is considerably simpler tlian the Bayes estimator, when the expected number of changes in the mean is neither zero nor n-i the Bayes estimator is more efficient than the MVLU. Chernoff and Zacks studied an alternative problem in which the prior distribution of time points of change is such that there is at most one change. This problem leads to a relatively simple Bayes estimator. However, difficulties may arise if this estimator is applied when there are actually two (or more) changes. The suggested technique starts at the entl of a series, searches back for a change in mean and then estimates tlie mean value of the series forward from the point at which such a change is assumed to have occured. They designed a procedure to test whether a change in mean has occurred and found a simpler test than the one used by Page (1954, 1955). Most of the results appearing in this paper were derived in a previous paper by Barnard (1959) in a somewhat different manner, but the general results are essentially the same. The previous paper by Chernoff and Zacks motivated some research in the follov/ing years. Mustafi (1968) considered a situation in which a random variable is observed sequentially over time and the distribution of this random variable is sub-jected to a possibl.change at every point in the sequence. The study of this problem is centered about the model introduced by Chernoff and Zacks.

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45 Three aspects of tlie prublem were considered ])y Mustafi. First he considered the i^robleni of estimating' the current value of the mean on the basis of a set of observations taken up to the present. Chernoff and Zacks assumed that certain parameters occuring in the model were kno^^m . Mustafi tlien derives a procedure for estimating the current value of the mean on tlie basis of a set of observations taken at successive time points wlien nothing is known about the other parameters occuring in tlie model. Second Mustafi estimated tlie various points of change in the framework of an empirical Bayes procedure and used an idea similar to tliat of Taimiter (1966) to derive a sequence of tests to be applied at each stage. Third he considers n independent observations of a random variable that belong to the one parameter exponential family taken at successive time points. He examines the problem of testing the equality of these n parameters against the alternative that the parameter has changed r-times at some unkno\^m points where r is some finite positive integer less than n. He developed a test procedure generalizing the techniques used by Render and Zacks (1966) and Page (19S5) . Hinich and Farley (1966) also studied the problem of estimation models for time series with nonstationary means. They assumed a model similar to the one developed by Chernoff and Zacks except that they assumed that the number of points of change per unit time are Poisson distriluited with a knovm shift rate parameter. They found an estimator for the mean which is unbiased and efficient. Also it turned out to be a linear combination of the vector of observations.

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46 The Farley-llinich technique attempts to estimate jointly the level of the mean at the beginning of a series as v;ell as the size of the change (if any) . Farley and Hinich in a later paper (1970) compared the method developed in (196h) with the one presented by Chernoff and Zacks (1954) and later generalized by Mustafi (1968). Some ways were examined to systematically track time series which may contain small stochastic mean shifts as well as random measurement errors. A "small" shift is one which is small relative to measurement error. Three approaches were tested with artificial data, by means of Monte Carlo methods, using mean shifts which were rather small, that is, mean shifts which \-iere half the magnitude of random measurement error variance. Several false starts with actual marketing data showed that there was an identification problem to provide an adequate test of the procedures' performance, and artificial data of known configuration provided a more natural starting point. Two techniques (one developed by the authors and the oth.er by Chernoff and Zacks) involved formal estimation under the assumption that there was at most one discrete jump in a data record of fixed length of the type often stored in an information system. Both techniques performed reasonably well when the rate of shift occurrence was known, but both teciiniques are very sensitive to prior specification of the rate at which shifts occur in terms of both classes of errors, that is, missing shifts wliich occur and idenc living "shifts" which do not occur. Knowing the shift rate precisely and knowing that more than one siiift in a record

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A7 is extriimely unlikely are two very severe restrictions for many applications. A sin.pler filter technique was tested siriiJ.irly witli more promising results in terms of avoiding both classes of errors. The filter approach involved first smoothing the series and then implementing ad hoc decision rules based on consecutive occurrences of smoothed values falling outside a predetermined range around the moving average. Harrison and Stevens have produced two important papers about Bayesian forecasting using nonstationary models. In the first of these papers (1971), they described a new approach to short-term forecasting based on Bayesian princiiiles in conjunction with a multi-state datagenerating process. The various states correspond to th.e occurrence of transient errors and step changes in trend and slope. The performance of conventional systems, like the growth models of Holt (1957), Brown (1963) and Box-Jenkins (1970), is often upset by tlie occurrence of changes in trend ,uul slope or transients. In Harrison an
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A8 formation in a purely mechanica]. way. The latter, however, includes people: the person responsible for the foret:ast and all the people concerned with using tlie forecasts and supplying information relevant to the resulting actions. It is necessary that people can communicate their information to the method and that the method clearly communicates the uncertain information in such a way that it is readily interpreted and accepted by decision makers. The basic model, called by them "the dynamic linear model", is defined together with Kalman filter recurrence relations and a number of model formulations are given based on their result. They first plirase the models in terms of their "natural" parameters and structure, and then translate them, into the dynamic 1 inear model form. Some of the models discussed by them are, a) regression models, b) the steady model, c) the linear growth model, d) the general polynomial models, e) seasonal models, f) autoregressive models, and g) moving average models, Multiproct^ss models introduce uncertainty as to the underlying model, itself, and this approach is described in a more general fashion than in tlieir 1971 paper. In the 1976 paper they present a Bayesian approach to forecasting which not only includes many conventional metliods, as presented before, but possesses a remarkable range of additional facilities, not the least being its ability to respond effectively in the start-up situation where no prior data history (as distinct from information) is available. The essential foundations of the method are: (a) a parametric (or state space) model, as distinct from

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49 a functional model; (b) probabilistic information on the parameters at any given time; (c) a sequential model definition which describes how the parameters change in time, both systematically and as a result of random shocks; and (d) uncertainty as to the underlying model itself, as between a number of discrete alternatives. Kamat (1976) developed a smoothed Bayes control procedure for controlling the output of a production process when the quality characteristic is continuous with a linear shift in its basic level. The procedure uses Bayesian estimation with exponential smoothing for updating the necessary parameter estimates. The application of the procedure to real life data is illustrated with an example. Applications of the traditional x-chart and the cumulative sum control chart to the same data are also illustrated for comparison. In Chapter Three of this dissertation we develop a Bayesian model of nonstationarity for normal and lognoi'mal processes. We build our results directly on two papers, Winkler and Barry (1973) and Barry and Winkler (1976). In the first paper they developed a Bayesian model for nonstationary means in a multinormal data-generating process and demonstrated that the presence of nonstationary means can have an impact upon the uncertainty associated with a given random variable that has a normal distribution. Moreover, the nonstationary model considered by

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50 them seems to have more realistic properties than the corresponding stationary model. For example, they found that in the nonstationary model the recent observations are given more weight that the distant ones in determining the mean of the distribution at any given time, and the uncertainty about the parameters of the process is never completely removed. Barry and Winkler (1976) were concerned with the effects of nonstationarity on portfolio decision. The use of a Bayesian approach to statistical inference and decision provides a convenient framework for studying the problem of changing parameters, both in terms of forecasting security prices and in terms of portfolio decision making. In this thesis a number of extensions to their results are made, thereby removing some of the restrictiveness of their results, and applications are considered in the areas of CVP analysis and life testing.

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CHAPTER THREE NONSTATIONARITY IN NORMAL AND LOGNORMAL PROCESSES 3. 1 Introduction The normal distribution is considered by many persons an important distribution. Tlie earliest workers regarded the distribution only as a convenient approximation to the binomial distribution. However, with the work of Laplace and Gauss its broader theoretical importance spread. The normal distribution became widely and uncritically accepted as the basis of much practical statistical work. More recently a more critical spirit has developed, with more attention being paid to systems of "skew (asynmiet ric) frequency curves". This critical spirit has persisted, but is offset by developments in both theory and practice. The normal distribution has a unique position in probability theory, and can be used as an approximation to many other distributions. In real world problems, "normal theory" can frequently be apjjlied, with small risk of serious erros, whtni substantially non-normal distributions correspond more closely to observed values. This allows us to take advantage of the elegant nature and extensive supporting numerical tables of normal theory. Most theoretical arguments for the use of the normal distribution are based on forms of central limit tiieorems. These theorems state conditions under which the distribution of standardized sums of random variables tends to a unit non.ial d isi r i ImL ion as the number of variables in tlie sum increases, that is with conditions sufficient to ensure an asymptotic unit normal distr ibut ion. 51

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52 The normal dist rlbul ion , for the reasons exposed before, has been widely used and enumerating the fields of application would be lengthy and not really informative. However, we do emphasize that the normal distribution is almost always used as an approximation, either to a theoretical or an unknown distribution. The normal distribution is well suited to this because its theoretical analysis is fully worked out and often simple in form. Where these conditions are not fullfilled substitutes for normal distributions should be sought. Even when normal distributions are not used results corresponding to "normal theory" are often useful as standards of comparison. The use of normal distributions when the coefficient of variation is large presents many difficulties in some applications. For instance, observed values more than twice the mean would then imply the existance of observations with negative values. Frequently this is a logical absurdity. The lognormal distribution, as defined in equation 2.2.20, is in at least one important respect a more realistic representation of distributions of characters that cannot assume negative values than is the normal distribution. A normal distribution assigns positive probability to such events, while the lognormal distribution does not. The use of the lognormal distribution has been investigated as a possible solution to this problem [see Cohen (1951), Gallon (1Â«79), Jenkins (1932) and Yuan (1933)]. In a review of the literature Gaddum (1945) found that the lognormal distribution i;oulil ht used to describe several processes. In Chapter Two we presented a list of some of the applications of this distribution

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53 to real life problems. Among those applications we emphasized its use in Cost-Volume-Profit analysis and in life testing models. Furthermore, by taking the spread parameter small enough, it is possible to construct a lognormal distribution closely resembling any normal distribution. Hence, even if a normal ditribution is felt to be really appropiate, it might be replaced by a suitable lognormal distribution. As v;as mentioned in Chapter Two, most research concerned with the normal and lognormal distributions has considered only stationary situations. That is, tlie parameters (known or assumed to be known) and distributions used are assumed to remain the same in the future. In this third chapter we intend to build a nonstationary model for normal and lognormal processes from a Bayesian point of view. Section 3.2 sets the stage for the development of the nonstationary model. In it, we describe essential features of the Bayesian analysis of normal and lognormal processes including prior, posterior and predictive distributions. Two uncertainty situations are considered in this section; In one the shift parameter, \i , is assumed to be unknown and the spread parameter, a, is assumed to be known; and in the other, both parameters are assumed to be unknown. In Section 3.3, we develop a particular nonstationary model for the shift parameter of the lognormal distribution, again under the same two uncertainty situations, and provide a comparison of the results with a stationary model.

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54. 3.2 B ayes la n A nnly sis of Normal a nd Lo^^normal Proc esse s lief ore the last decaiie, most of the Rayeslan research deiiling with problems of statistical inference and decisions concerning a parameter assume that G takes on a single value; those models are called stationary models. For example, 6 may represent the proportion of defective items produced by a certain manufacturing process; the mean monthly profits of a given company; the mean life of a manufactured product and so on. In each case 6 is assumed to be a fixed but not knovvoi. A formal Bayesian statistical analysis articulates the evidence of a sample to be analyzed \.'ith evidence other than that of tlie sample; it is felt that there ususally is prior evidence. The non-sample evidence is assessed iudgmentally or subjectively and is expressed in probabilistic terms, by means of: (1) a data distribution tliat specifies the probability of any sample result conditional on certain parameters; and (2) a prior distribution that expresses our uncertainty about the parameters. When judgment in the form, of the assessment of a likelihood function to apply t(j the data is combined with evidence of a sample, we have the likelihood function of the sample. The likelihood function of tlie sample is combined with the prior distribution via Bayes' theorem to produce a posterior distribution for the parameters of the data distribution, and this is the typical output of a formal Bayesian analysis. If we assume that the prior distribution, for the parameters of the data Â»! i st rihution , is continuous then we may express^ Bayes' theorem as

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55 where and [i.Z.i) l(y x) ' . I . ' , t(x i) + 0; ' 1 (x I t) ' ' X denotes the vector of sample observations, 6 represents all the unknown parameters, r represents the known parameters of the prior distribution of 0. We can interpret f(x|o) in two ways: (1) for given e, f(x|o) gives the distribution of the random vector s; (2) for given x, f(x|6) as a function of u , together with all positive multiples, in the ususal usage is the likeJ ihood function of the sample. The prior i^robahility of the sample fCxji) is computed from (3.2.2) fCxIr) = / f(e|T) f(x|9) de , (J from which we see tliat f(xli) can be interpreted as the expected value of the likelihood in the light of the prior distribution. Alternatively, f(x|T) can be interpreted as the marginal distribution of the random vector x with respect to the joint distribution, (3.2.3) f(x,6|T) = f(e|T) f(x|e). Since (3.2.2) can be computed in advance of the sample for any x, we shall frequently refer to tlie marginal distribution of x as the predictive distr ilniL ion implied by the specified prior distribution and data distribution.

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56 If we have a posterior distribution f(e|x) and if a future random vector S is to come from f(x |o), whicli may or may not be the same data distribution as in ('3.2.2), we may compute (3.2.4) f(x |x) = / f(0|x) f(x |e) de. We refer to the distribution so defined as tlie predictive distribution of a future sample implied by the posterior distribution. It must be understood that (3.2,2) and (3.2.4) are but two Instances of the same relationship; sometimes it is worth distinguishing the practical problems arising when predictions refer to the present sample from those arising in connection with predictions about a future sample; that is a "not-yet-observed" sample. The revision of the prior distribution gives the statistician a method for dravv'ing inferences about 9, the uncertain expression, quantity or parameter of interest, and for decisions related to 6. In general then we may say that the term Bayesian refers to any use or user of prior distributions on a parameter space (although there is some nonpnrametrlc Bayesian material also) with the associated application of Bayes theorem in the analysis of an inferential or decision problem under uncertainty. Such an analysis rests on the belief that in most practical situations the statistician will possess some subjective a priori information concerning the probable values of the parameter. This information may often be reasonably summarized and formalized by tlie choice of a suitable prior distribution on the parameter space. The fact that the decision maker can not specify every detail of his prior distribution by direct asses-

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57 snieiit means that t lic^rc will otien be considerable latitude in the choice of the family of distributions to be used, even though tlie selectioii of a particular member within the chosen family will usually be wholly determined by the decision maker's expressed beliefs or betting odds. Three characteristics are particularly desirable for a family of prior tiistributions : (i) analytical tractability in tliree aspects; namely
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58 cliosen member of tlie family is really in close agreement with the decision maker's prior jiuigments about and not a mere artifact agreeing with one or two quantitative sununarizatlons of these judgments. A family of prior densities which gives rise to posteriors belonging to the same family is very useful inasmuch as one aspect of mathematical tractability is maintained, and this property has been termed "closure under sampling". For densities which admit sufficient statistics of fixed dimensionality, a concept to be explained later, Raiffa and Schlaifer (1961) have considered a method of generating prior densities on the parameter space that possess the "closure under sampling" property. A family of such densities has been called by them a "natural conjugate family". To define the cont;epts of sufficient statistic and sufficient statistic of fixed d im.ensional Ity , consider a statistical problem in which a large amoimt of experimental data has been collected. The treatment of the data is often simplified if the statistician computes a few numerical values, or statistics, and considers these values as summaries of the relevant information in the data. In some problems, a statistical analysis that is based on these few summary values can be just as effective as any analysis that could be based on all observed values. If the summaries are fully informative they are known as sufficient statistics. Formally, suppose tliat G is a parameter which takes a value in the space 0. Also suppose that x is a random varialile, or random vector, which takes values in tlie

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59 sample tipacu? S. We slin 1 1 let '^(Â•|0,^) dfnote the conditional probability density finu't ion (p.d.f.) of x wlien 6 = 0^ (G,-t:0). It is assumed th.it the oljserved vaJue of x will be available for making inferences and decisions related to the parameter Q. Denote any function T of the observations x, a statistic. Loosely speaking, a statistic T is called a sufficient statistic if, for any prior distribution of 6, its posterior distribution depends on the observed value of X only through T(x) . More formally, for any prior p.d.f. gCe) and any observed value xeS, let g(*|x) denote the posterior p.d.f. of 0, assuming for simplicity that for every value of xeS and every prior p.d.f. g, the posterior g('|x) exists and is specified by the IWiyes theorem. Then it is said that a statistic T is sufficient for the family of p.d.f. 's f(-|0), QcQ, if g(*|x ) = g(-|x ) for any prior p.d.f. g and any two points x,eS and xÂ„E.S such that T(x,) = T(x,-,) . Now, consider only data generating processes which generate independent and identically distributed random variables x , x , ... , such that, for any n and any (x^ , xÂ„, ... , x ) there exists a suf1 z . n ficient statistic. Sufficient statisticsof fixed dimensionality are those statistics '1' such that T (x x^, ... , x ) = T = (T , T ... , T ) where a particular value T. is a real number and the dimensionality s of T does not depend on n. Independently of how many elements we sample, only s statistics are needed. Kaiffa ami Sclilaifer (1961) present the following metliod for develojjing the natural coniug.ate ]n"ior for a given likelihood function:

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60 (i) Let the density function of 6 be g, where g denotes eithei" a pi-ior or a posterior density, and let k be another function on 6 such that Then we shall write (3.2.6) g(o) cc k(e) and say that k is a kernel of the density of 0. (ii) Let the likelihood of x given q be l(x|6), and suppose that P and k are functions on x such that, for all x and fcl, (3.2.7) l(x|e) = k(x|e) P(x). Then we shall say that k(x|6) is a kernel of the likelihood of x given 6 and that P(x) is a residue of this likelihood. (iii) Let tlie prior distribution of the random variable 6 liiive a density g'. For any x such taht l*(x|g|) = / 1 (x 1 6) g' ( 6) d e > 0, 6 it follows from Bayes theorem that the posterior distribution of 9 has a density g" whose valurat ( 6) for the given x is (3.2.8) g"(0|x) = g'(e) l(x|e) N(x) , wliere N(x) = [ / g'(G) KxIh) de]~^ .

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61 (iv) Now 1ft k' deni)te a kernel of the prior density of 6. It follov^is Troni the definitions of k and I and of the .syniiiol Â« that the Bayes formula can he written, (3.2.9) g"(f)|x) = g'(e) 1(x|b) N(x) = k'(e) [ / k(G) de]~^ k(x|e) P(x) N(x) e 8"(g|x) cc k'(6) k(x|o), where the value of the constant of proportionality for the given x, (3.2.10) P(x) N(x) [ / k(e) do]"^ 6 can always be determined by tlie condition, (3.2.11) g"(o|x) d8 = 1, whenever the Integral exists. Before v/e begin our presentation of a basic Bayesian analysis of normal and lognormal processes we want to emphasize that caution should be exercised in the application of the method developed by Raiffa and Schlaifer, as is pointed out by Box and Tiao (1972). According to them it is often appropiate to analyze data from scientific investigation on the assumption that the likelihood dominate the prior, for two reasons : (i) a scientiLic investigation is not ususally undertaken unless information supplied by the investigation is likely to be considerably more [irecise tiiaii information already available, that is unless it is Likely to increase knowledge by a substantial amount. Therefore analysis

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62 Vv^ith priors which are dominated by the likelihood often realistically represents the true inferential situation. (ii) Even when a scientist holds strong prior beliefs about the value of a parameter 6, nevertlieless, in reporting the results it would usually be appropiate and most convincing to his colleagues if he analyzed the data against a reference prior which is dominated by the likelihood, lie could say that, irrespective of what he or anyone else believed to begin with, tlie posterior distribution represented what someone who a priori knew very little about 9 should believe in the light of the data. Reference priors in general mean standard priors dominated by the likelihood. [See Dickey (1973) for a general discussion of Bayeaian methods in scientific reporting.] In general a prior wliich is dominated by the likelihood is one which does not change very much over the region in which the likelihood is appreciable and does not assume large values outside that range. We shall refer to a prior distribution which has these properties as a locally uniform prior. There are some difficulties, however, associated with locally uniform priors. The choice of a prior to characterize a situation where "nothing" (or, more realistiqal ly , little) is known a priori has long been, and still is, a matter of dispute. Bayes tentatively suggested that where such knowledge was lacking concerning the nature of the prior distribution, it might be regarded as uniform. There is an objection to Bayes postulate. If the distribution of a continuous parameter b were taken to be locally uniform, then the distribution of ., log 6, or some other transformation of 9 (which might provide equally

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63 sensible bases for parametrizing the problem) would not be locally unifonu. Thus, nppplication of Bayes' postulate to different transformations of G would lead to posterior distributions from the same data whicli were inconsistent with the notion that nothing is known about 9 or functions of Â„ This argument is of course correct, but the arbitrariness of the choice of parametrization does not by itself mean that we should not employ Bayes postulate in practice. Box and Tiao (1972) present an argument for choosing a particular metric in terms of which a locally uniform prior can be regarded as noninformative about the parameters. It is important to bear in mind that one can never be in a state of complete ignorance; further, the statement "knowing little a priori" can only have meaning relative to the information provided by the experiment. A prior distribution is supposed to represent knowledge about parameters before the outcome of a proiected experiment is kno^^m. Thus, the main issue is how to select a prior which provides little information relative to what is expected to be provided by the intended experiment. 3 . 3 No nstat ionary Model for Normal and Logno rmal Mean s It was emphasized in Section 2.3 that for many real world data generating processes the assumption of stationarity is questionable. Random parameter variation could be a reasonable assumption when we are concerned with life testing models or witli economic variables. For example, iw life testing models, when it is assumed that the life of certain parts follows a lognormal distribution, the stationarity

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64 assumption could be exjiocted to hold over short periods of time; but in most cases it would be expected that for a lengthy period, stationarity would be a doubtful assumption. Similarly in other areas like Cost-Volume-Profit analysis it is doubtful that the stationarity assumption will hold over long periods of time. Variables like sales, costs, and contribution margin are affected by economic, political and environmental factors. In particular it was pointed out that we are interested in gradual changes, the effects of which are not perfectly predictable in advance for a particular period. If a data generating process characterized by some parameter e is nonstationary , then it is potentially misleading to make inferences and decisions concu^^rning ti as if 6 only took on a single value. Instead we should be concerned witli a sequence 6 , 6Â„, ... , of values of 6 corresponding to different time periods, assuming the characteristics of the process may vary across time. Several methods have been proposed to study stochastic parameter variation [see Chernoff and Zacks (1964) and Harrison and Stevens (1976)]. Some have claimed that a reasonable approach to the effects of gradual change might be to model the parameters of nonstationary distributions as if they undergo independent random shifts through time [see Barry (1976), Carter (1972), and Kamat (1976)]. Specifically they suggest the use of a model that assumes that the mean of the distribution has a linear shift. In those papers, it is clearly demonstrated that when it is assumed that the process represented by the model is normal, this linear random shift model allows analytical comparisons to be drawn if it is assumed that the succesive incri^ments in the princess mean are drawn independently

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65 from a normal population with mean u and variance p. We Intend to use the same approat'li in this dissertation. Two cases are considered: IJ unknown and o" known; and both y and ounknown. 3.3.1 p is Unknown and o^ is Known For a process that has a normal density function with unknown parameter u, Raiffa and Schlaifer (1961) show that the natural coniugate prior is normal with parameters m' and o'^/n'. (See Appendix I for the details of their exposition.) From the prior distribution on 0Â„ and with a sequence of n independent observations (x , xÂ„, ... , x ) from tlie normal process under consideration [N(p,o''-)], the posterior distribution in period zero is obtained. If the sample yields sufficient statistics m and n, then the posterior distribution is normal with parameters n" and m'' jj,lven by (3.3.1) n^ = n^ + n, and (3.3.2) m|J = (n^ m'^ + n m)/(n^ + n) . If the mean of the distribution does not change from period to period except by the effect of the sample information then each posterior can be thought of as prior with respect to the following sample. Thus, the posterior distribution on p is the prior distribution on p ; i.e. (3.3.3) i'^ (^'(M-\y "^/"o) = f?; (Miin>[, o'7np ,

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66 where (3.3.4) ra[] = m| , and (3.3.5) nJJ = n| . In general, if we assume that a fixed sample of size n is employed every time a sample is taken and if we assume that the mean is stationary except by the effect of the sample information, then in any given period t the posterior distribution is normal with parameters n" and ra" given by, (3.3.6) n'^ = n' + n , and (3.3.7) m" = (n' m' + n m)/(n' + n) . This inferential model is called a stationary model since it assumes that neither the distribution nor the parameters change from period to period. In this case it assumes that \i takes on the same value in every period and that f'(y) represents the information available about that value as of the start of the t-th period. Suppose now that the process generating the observations undergoes a mean shift between succesive periods. In particular inferences about the mean of a normal process are considered when the parameter n shifts from period to period, with the shifts governed by an independent norma] process. Formally, consider a data generating process that generates n observations x ,, x Â„, ..., x during time * ti t2 ' tn

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67 period t accordinj', to a nornuil process wiLli parameters p and o^. Assume that the parameter o is known antl does not chanj^e over time, whereas p is not known and may vary over time. In |Kirtionlar, values of the parameter tor successive time periods are related as, (3.3.8) p^^^ = p^ + e^_^^, t = 1, 2, ... , where e is a normal "random sliock" term independent of p with known mean u and variance a^.That is u behaves as a random walk. e "^t The mean in any period t is equal to the mean in the previous period plus an increment e, wh.ich has a normal distribution, with known mean and variance. Before the sample is taken at time t, we assume that a prior density function t:ould be assessed that represents judgment (based on past experience, past information etc.) concerning tlie probabilities for the possible values of p . If tlie prior distribution of p at the beginning of time period t is represented by f'(p ), and a sample of size n during period t yields x = (x ,,..., x ), then the prior distribution of u can be revised. Furthermore at the end of time ^t period t (the beginning of time period t+1 ) " the data generating process is governed by a new mean p ,, so it is necessary to use the posterior distribution of p and the relation (3.3.8) to determine tlie prior distribution of p . Jn order to determine the distribution of the parameter p ,-, a we] L kutivjn tlieoic'iii cculd be used. It says that tiie convolution g(2) of tvr/o normal distributions with parameters (p oO and (p,,,(3^)

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68 gives a distribution which is normal with mean (p, + \i^) and variance (o2 + o|) , i.e. , (3.3.9) g(z) = f (z|m;^ + P,, o2 + op [see Mood et. al . (1974)]. Thus the distribution of fi , is normal, i.e. , (3.3.10) ^N('\+iht + "' (Â°^/"t) + Â°e^' -"< f^t+1 ^'"' _co< m" + u <Â«, (a^/np + a2 >0. We could find a simpler expression if we realize that, since o and a^ are positive, there must exist n such that, (3.3.11) q2 = o2/n^ , or n = o^/o^ s e In other words, the disturbance variance is a multiple of the process variance. The prior distribution of the mean after t periods then simplifies to (3.3.12) fN^\+ll"'t + ^' ^^f("t + "s^/"t "s^^' or where (3.3.14) m'_^j^ = m;: + u.

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69 and (3.3.15) n' , = In" n /(n'/ + n ) ] Â•Â• n" . t+1 t y t s t The inequality stated above can be interpreted as showing tliat the presence of nonstationarity produces greater uncertainty (variance) at the start of period t+l than would be present under stat ionarity because in the stationary case n' = n" . If we assume that a change in the mean occurs between every tvro consecutive periods then we could repeat the previous procedure each time a change occurs to determine the new prior distribution. For a process tliat has a lognormal density function as defined in (A1.14), it was shown in Appendix I that, when the unknovjn parameter is p, the natural conjugate prior is normal. Thus, the revision of the prior distribution in ;uiy given period is identical to the revision in the normal case [see equations (3.3.6) and (3.3.7)] except tliat m is defined as the sample mean of the natural logaritlims of the observed X values. Furthermore the procedure presented before to represent changes in the mean, p, of the normal distribution can be used to model changes in the shift parameter p of the logrtormal distribution. The normality of the natural conjugate prior, in this case, allows us to use the formulas ( i. 3. 8)-( 3. 3. 15) to study the behavior of the prior distribution of y after t jjeriods of time. Since tinvariance V(x) of the lognormal random variable x is a function of p and a'' in the lognormal case, nonstationarity in p means that both the mean and the variance of x are nonstat ionary , so

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7G that the lognormal case provides a generalization of the normal results, 3.3,2 y a nd o Bo th Un know n The results of the previous section can be extended to the case of unknown mean and variance. The joint natural conjugate prior density function for y and o^ is a normal-gamina-2 functions, as was shown in Appendix I, given by (3.3.16) ^-1 /n' exp[Â— 2(p-m')] exp[--Â— ^] [~^jz] [-JÂ— ^ a /2tt r(d'/2) Given a prior from this family and assuming that information is available from a normal (or lognormal) process through a sample of observations X , x^, . . . , X , it is possible to obtain a posterior distribution of the two parameters p and 5^. It was shown in Appendix I that the posterior distribution is also normal-gamina-2, i.e., f'' Â„ (^,6^ Im" ,v" , n" ,d") where (3.3.17) m" = (n'm' + n m)/(n.' + n) , (3.3.18) v" = [d'v' + n'm'+ dv + nm2 n"m"2]/(d' + n) , (K3.19) n" = n' + n ,

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71 and (3.3.20) d" = d' + n, It is clear from (3.3.16) that the joint distribution of \i o and a Is the product of two marginal distributions, i.e., (3.3.21) r_ _^Cu,a^\m'\v",n",d") = f " (p | o^n" ,m") f"(d2|v",d") The marginal density of 5^ does not depend on jj. Now consider the case of nonstationary y as in the previous section. The independence of the marginal distribution of o'^ from u will be an important factor in our results below. At the end of period t (the beginning of time period t+1) the posterior distribution of p and o^ could be used in conjunction with the relation between p and the random shock e to get the joint prior distribution at the beginning of period t+1. As before, the random shock model to be considered is u = u + e .We make the assumpt+1 t t+1 tion that although 6 is unknown, it is known that e 's variance, t a , is 1/n times the unknown process variance, a . As before, assuming that ti has a posterior distribution with parameters (m'^o^/n") and that e is distributed normally with parameters (u,d^/n ) it was shown in Appendix I that the convolution z (z = u + e) has a conditional density given by (3.3.22) g(z) = f:;(z|m" + u, a'^[il/n") + (l/n,.)l).

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72 Note that this density is conditional on 5^, as is the conjugate prior of p. Thus, the prior density of P^.iÂ» at the beginning of period t+1 after the random shock has occured, is given by (3.3.23) f^(Pt+ih" + ". o2[(ng + np/n'^: nj). Since o^ is assumed constant, f ^(o^) does not change but Y-2 equals the posterior distribution at the end of period t. Hence, the joint distribution at the beginning of period t+1 is given by ^3-3. 2M f' _,(0t+l.^^) = fN(Pt+ll-t + u.52[(n3+ n'-n,)]) f ' z^^' I ^t. V') If we let (3.3.25) m^^^ = m;; + u, (3.3.26) n'_^^ = n'' n^/(:Vn''). (3.3.27) d^^^ = d'^. and (3.3.28) v^^^ = v', then the distribution of jj and o*^ could be written as The revision could be continued since the prior distribution at the beginning of period t+1 is still a normal-gamma-2 distribution. At any time t, the process mean is not known with certainty, but the

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7 3 informaL ii)n from tlie samples collected up to time t provides an indication of P . Before the sample is taken at time t, we assuiiit' that one is capable of assessing a prior density function that represents our judgment (based on past experience, past information, etc.) concerning the probabilities for the possible values of p and o"^. In effect, one viev;s ( m ,5 ) as a pair of random variables to which we have assigned a probability density function; in this case a normalgamma-2 with parameters m' n', v' and d' The sample results at time t can be described in terms of the sufficient statistics m. , n^^ , v and d ; sample mean, sample size, sample variance and degrees of freedom needed to determine v , respectively. Using these sample results, a new posterior distribution could be obtained whicli is normal-gamma-2 . The tractability of the model is maintained when a n^itural conjugate prior is used and ,i shifl model of the form (3.3.8) is assumed for the changes of the parameter M between tvjo consecutive periods. Hence, after t periods of time the joint distribution of p and a^ is normagamma-2; that is, ^^Â•^Â•^"^ ^^;-.-2^vi'''i">t-*-i' ";+!' ^t+i' <+p ' where (3.3.31) d^^^ = dj + (t)n , (3.3.32) n'^j = (n* + n)n^/[(n^ + n) + nj ,

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7A (Â•5.3.33) v' = fd'v' + n'm'+ dv + nm^ + n"m"^]/[d' + n), ^ 'Â• Â• ' i + \ 'll tt ttt and (3.3.3A) m^_^^ = (n^m^ + nm)/(n^ + n) . In this manner, a sequence of prior and posterior distributions for successive p may be obtained as successive values of the random vector S = (x, , .... X ) are observed. ^ Vlt' 'at For the process that has a lognormal density function as defined in (A1.14), it was shown before that when both parameters are unknown the joint natural conjugate prior is normal-gamma-2 , Tims, Che revision of the i^rior distribution in any given period is identical to the revision in the normal case. Furthermore the procedure presented previously to represent changes in the mean, P, of the normal distribution could be used to model clianges in the shift parameter P of the lognormal. The fact that both normal and lognormal distributions have a joint natural conjugate prior which is normal-gamma-2 allows us to use the formulas (3.3.30 3.3.34) to study the behavior of the prior distribution of P and 0after t periods, 3.3.3 Stationary Versus Nonstationary Results Stationary conditions, in the context of our discussion, imply that tiiere is no shift in the mean, M, of the distribution; that is, e = and consequt-ntly u and o are both zero. Successive values of V t e are tlie same acro.ss tinn-, i.e., P = M.-,= ... P . For the case when 1 2 t

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75 only (I is unknown, thlt; implies that equation (3.3.10) becomes, (3.3.35) f'/Pt+ll'^'t + ^' ("'/"P + "^' or (3.3.36) fNtpj.+Jm'^', (o2/n'')]. Under stationari ty , then, the prior distribution of fj^ii at the start of period t+1 is the same as the posterior distribution of p at the end of period t. In the case of nonstationarity with no drift, u=0; in other words, tlie distribution of e is normal with mean and variance o| Â• For this case it is clear that for a given posterior distribution of p at time t, the only difference between the prior distributions of P^,i under stationarity (see equation 3.3.36) and the prior distribution of u under nonstationarity (see equation 3.3.10) is the variance term. The prior variance of P , , under stationarity IS, (3.3.37) Varg(P^^j^) = ''^/n^+^ = Â°^/n'^; whereas the prior variance of \^-, under nonstationarity is, (3.3.38) Var^,^(P^^,) = o^/n' = (o'/n'l) + (o'/n ), = a^[(l/np + (1/n^) As expected, the incorporation of the nonstationary condition has caused an increase in the variance of the prior distribution. The variance increased by an amount "Vn^; that is, by an amount equal

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76 to the variance of the distribution of successive increments in the process mean. For th.e stationary case (3.3.39) i^[+i^~^ = n/n;;] , and for the nonstationary case, (3.3.40) t";^!^^ = [(1/np + d/n^)]. Thus, equivalent ly, we could say that for a given posterior distribution of p at time t, the only difference between the prior distribution of u , , under stationarity Is that the term n' , is larger '^t+1 ^ t+1 ^ with the stationary condition. When u=|=0, m' is alv^'ays changing and, therefore, tliere is a difference in mean and variance. Stationary conditions, in the case when both jl and 6^ are unknown, imply that in any given period t+1 the joint prior density for p and 5 is a normal -gamma-2 of the form given in equations (3.3.30 3.3.3A) . That is, (3.3.41) 'n-y-2^\.v''K.v ^;+i'"m'^m> ^N^ptVii^^+i'/-) ^'-2^^'|^^l'^;+l>' where (3.3.42) m' , , = m" r + 1 t (3.3.4 3) (3.3.44) V , , = V t+T t u , , = n t + 1 t

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77 and (3.3.A5) d'^^ = .r . Under stationai-ity , then, the joint prior distribution of p and d^ at the start of pi-riod t+1 is the same as the posterior distribution of p and (5^ at the end of period t. Since the distribution of a^ does not depend on jj, only on the parameters d and v, we could model changes in p. These changes in the mean only affect the function ^M^fif + l I'^'+l ' o"^/"'.!)' ^'^ equation (3.3.'41). In fact, the effect of the nonstationarity assumption on f^Cy , -, ) is identical to the effect of nonstationarity over the prior distribution in the case when only y was the unknown parameter. In the case of nonstationarity with no drift, i.e., u=0, for a given posterior distribution of p and a at time t, the joint prior density function for p and o'^ is similar to the stationary viounterpart , as given in equation (3.3.41), except for the fact that the variance of f'(p ,-, |ni',-,5 o^/n' ) is larger than the variance uf f'(p^.i|m' , g'^/n' ,) in the stationary case. N t+i t+1 t+1 In other words 6'^/n' in the stationary case is smaller than fl-^/n' in the nonstat ionary case. The nonstationarity assumption also affects the predictive distribution. For the case wlien p is the unknown parameter and the data generating process is normal, assume that after t periods we have a posterior tl i str i luit icjii f"(u ) wliich is norma] with mean m" and t ' L t variance o'/n". The predictive distribution at the end of period t was shown in equal ion (Al..i2) to be nnrin.il with moan,

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(3.3.A6) ''-t^^t^ " "" ' and variance (3.3.47) VarJXj.) = a2l(l + np/n;*] = o2[l + (1/np]. If the process is stationary then the predictive distribution of the random variable of interest at the beginning of period t+1 is the same as the distribution we had at the end of period t, i.e., N(m" , o^ [ (1+np /n|_' ] ) However if we assume the nonstat ionary condition, the prior distribution of p at the start of period t+1 has a different mean and a different variance. Consequently the predictive distribution changes in mean and variance between consecutive time periods. In other words E , (x ) is always changing depending on the stochastic change of the mean p , , Â• In the case of nonstationarity with no drift, i.e., u=0, for a given posterior distribution of y at time t, the only difference between the predictive distribution of x ^^ under stationarity and tlie predictive distribution of x under nonstationarity is the variance term. Thevariance of x under stationarity, at the start of time period t+1, is (3.3.48) Var^^^(x^_^^) = o2 [ (1+n;^^) /n^^^] = o^ [l+( 1/n;^^) ] . It was stated previously that the parameter n' is smaller when y is unknown and nonstat ionary than when p is unknown but stationary. Hence, as expected, tlie variance of the predictive distribution, Var , (x ,), is larger when Q is nonstat ionary , This has some t+1 t+1 ' ' h M implications for t tie determination of prediction intervals; which

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79 we will discuss in detail in Chapter Fonr. Nonstat ionar i ty implies greater uncertainty, which is reflected by an increase in the measure ot uncertainty, variance. For the case when both p and 6 are the unknown parameters and the data generating process is normal, assume that after t periods we have a posterior distribution f"(p ,5^) which is normal -gamma2 with parameters m" , n", v" and d". The predictive b I t t t t "^ distribution at the end of period t was shown in equation (A1.33) to be Student with mean, (3.3.49) E (x ) = m" , d" > 1, t t t t ' and variance (3.3.50) Var^(x^) = [v'^ (n'^+1) /n^ [d;_7(d'^' -2)], d^,' > 2. Again, if the process is stationary then the predictive distribution at the beginning of period t+1 is the same as the distribution that we had at the end of period t, i.e., ST (m" , [v" (n"+] ) /n" ] [d'7 (d" 2)]) When we assume the nonstationary condition, the joint prior distribution of p and 5"^ at tlie start of period t+1 changes from its original form at the end of period t. The specific random model we are assuming causes the parameter m and n of the distribution of p to change from the end of period t to the start of period t+1. Therefore the predictive distribution f' (x ,) has a different t+1 t+1 mean and variance than f"(x ). In tlie case of nonstat ionarity with It no drift, i.e., u=0, for a given posterior distribution of p and 6

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80 at time t, the only dillerence between the predictive distribution of X under station;iri ty vis-a-vis nonstationar ity is the variance term. Observing equation (3.3.50) closely we note that the effect of nonstationarity is the same as in all previous cases; that is the parameter n' is smaller wlien p is nonstationary and therefore the variance is larger. In this case since both p and o'^ are unknown, at the end of period t our estimate of the variance is v" which includes ail the t information that we have available at the time including sample information. A comparison of stationary versus nonstationary results when the data generating process is lognormal moves along tlie same lines as the normal process does. For the case where the unknown parameter is p, the nonstationarity condition causes an increase in the variance and in the mean of the normal prior distribution which causes an increase in the mean and variance of the lognormal predictive distribution. Similarly, for tlie case when botli parameters are unknown the condition causes an increase in mean and variance in the prior distribution of p and a change in the joint prior distribution of p and o^ which affects the logStudent predictive distribution. The logStudent predictive distribution has infinite mean and variance which are not affected by the nonstationary condition. 3. 4 Conclusion In this i.hapLer we modeled nonstationarity in the mean of normal and iognoriual processes under two uncertainty assumptions,

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81 The model is bullr upon the Bayesian analysis of in)rnia] processes of [\aifri and Schl.iiiiM' (lOhl) and upon the analysis of nonstat ionnry means of normal processes, for unknown fi , of Barry (197'3). We extended the nonstationary results of Barry (1973) to the lognormal distribution. The variance of the lognormal distribution is given by (3.4.1) Var(x) = w(v^-l) e2M , where w = 6x9(0^). Since V(x) is a function of p and o^ in the lognormal case, nonstationarity in jj means that both mean and variance of x are nonstationary, so that the lognormal case provides a generalization of the normal results. Furthermore, we developed ttie nonstationary model for the mean of normal and lognormal processes for the case when both parameters, p and Q, are unkno^^m. For each group of assumptions we noted that, in every time period t, the uncertainty is never fully eliminated from the model. In Chapter Two vje emphasized that the exponential distribution was often used to represent life testing models. All the researcli in the area of life testing vjhere this distribution has been used has assumed stationary conditions for the parameters of the model and for the model itself. Appendix II shows the Bayesian modeling of nonstat ionarity for the parameters of an exponential distribution using random shock models. On]y under very trivial assumptions does the analysis yield tractal)le and consequently useful results. On the other hand, as vjas shown in this chapter, the normal

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82 and lognormal distributions provide results that are especially tractable. In any given period t, the prior, posterior and predictive distributions depend on the parameters, m and n when only y is unknown; and on the parameters m , n , v and d when both y and a^ are unknown. Under the nonstationarity conditions, these parameters change from period to period not only because new information becomes available through the sample, but because of tlie additional uncertainty involving tlie shifts in the parameter y. To make better use of these distributions the decision maker must know how they are evolving through time. Management requires realistic and accurate information to aid in decision making. For instance the decision maker can be interested in knowing how the variance of the distribution of the mean, y, changes across time. Furthermore, since one of the objectives of the user of the distribution is to construct prediction intervals for the process variable he can be interested in knovv)ing how the variance of the predictive distribution behaves as the number of observed periods increases. We will address this problem in detail in Cliapter Four through the study of the limiting behavior of the parameters m , n , v and d . In addition, attention ^ t t' t t will be focused on the methods of constructing prediction intervals for tlie normal. Student, lognormal and logStudent distributions under various uniertaiaty conditions.

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CHAPTER FOUR LIMITING RESULTS AND PREDICTION INTERVALS FOR NONSTATIONARY NORMAL AND LOGNORMAL PROCESSES 4 . 1 Int r oduction In Chapter Three we emphasized that for many real world data generating processes the assumption of stationarity is questionable and stochastic parameter variation seems to be a reasonable assumption. If a data generating process characterized by some parameter is nonstationary, then it is potentially misleading to make inferences and decisions concerning the parameter as if it only took on a single value. We should be concerned with a sequence of values of the parameter corresponding to different time periods. It was shown in Chapter Three that if we use a particular stochastic model we can model nonstationarity for the shift parameter of normal and lognormal processes from a Bayesian viewpoint, under two uncertainty conditions, and that we can obtain tractable results. In particular, values of the parameter for successive time periods are assumed to be related as (^Â•1-1) Pt+l ^ Pt ^ ^+1' t = 1, 2, ... , where e , ^ is a normal "random shock" term independent of y with known t+1 ^ t mean u and variance o^ . The mean in any period t is equal to the mean in the previous period plus an increment e, which has a normal distribution, with known mean. Comparing tlie stationary with the nonstat ionary prcjcesses we pointed out tliat when tlie data generating process is normal or logH3

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84 normal and the unknown parameter is y, the nonstationary condition causes in any given period t an increase in the variance of the normal prior distribution. This causes an increase in the mean of the normal predictive distribution for normal processes and causes an increase in the mean and variance of the lognormal predictive distribution for lognormal processes. Wlien both parameters, y and q^ , are unkno^^m a similar result is found for the prior and predictive distributions of the normal and lognormal data generating processes. The results discussed in Chapter Three Viave to do with the period to period effects of random parameter variation upon the prior and predictive distributions. However, the asymptotic behavior of the model has important implications for the decision maker. For instance, when only y is the unknown parameter, under constant parameters uncertainty about y eventually is eliminated since nl Increases without bound and the sequence of prior variances (a^/n!) converges to zero. Hence the distribution of y eventually will be unaffected by further samples. On the other hand, shifting parameters could increase the uncertainty under which a decision must be made since it reduces the information content that past samples offer for the actual situation. Increases in uncertainty, caused by stochastic parameter variation, have important implications for the decision maker since his decisions depend upon the uncertainty under which they are made. Similarly, random parameter variation produces important differences in the limiting beliavior of the prior and predictive distributions wlien y and o^ are the unknovxm parameters. In Section 4.2 we studv the limiting behavior of the param-

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85 eters m' , v' n' and d' of the prior and predictive distributions for t L t t the normal and lognormal. data generating processes. In addition we discuss the implications of these limiting results for the inferences and decisions based on the posterior and predictive distributions. In any period t, all the information contained in the initial prior distribution and in subsequent samples is fully reflected in the posterior and the predictive distributions. In some applications, partial summaries of the information are of special importance. One important way to partially sunmiarize the information contained in the posterior distribution is to quote one or more intervals whicli contain a stated amount of probability. Often the problem itself will dictate certain limits whicli are of special interest. A rather different situation occurs when there are no limits of special interest, but an interval is needed to show a range over which "most of the probability lies". One objective of this thesis is to develop Bayesian prediction intervals for future observations that come from normal and lognormal data generating processes. In particular, we are interested in most plausible Bayesian prediction intervals of cover 3 as were defined in Section 2.2. In Section 4.3 we discuss the problem of constructing prediction intervals for normal, Student, lognormal and logStudent distributions. It is pointed out that it is easy to construct these intervals for the normal and Student distributions but that it is rather difficult for the lognormal and logStudent distributions. An algorithm is presented to compute the Bayesian i)rediction intervals for the lognormal and logStudent distribuLions. In addition, we discuss the relationship that

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86 exists between Bayesian prediction intervals under nonstationarity and classical certainty equivalent and Bayesian stationary intervals. 4 . 2 S pecial Prop erties and Limiting Results U nder Nonstati onarity 4.2.1 Limiting Behavior of m' and n' When \i is the Only Unknown Parameter For a process that has a normal density function with unknown parameter p, Raiffa and Schlaifer (1961) show that the natural conjugate prior distribution is normal with parameters m' and o^/n'. In Section 3.3 we pointed out that if the mean, y, of the data generating process does not change from period to period except by the effect of the sample information, then each posterior can be thought of as a prior with respect to a subsequent sample. In general, if we assume that a sample of size n is employed every time a sample is taken [which yields a n statistic m = ( Z x ./n)] and if we assume that the mean y is stai=l ^^ tionary then in any given period t the posterior distribution of y is normal with parameters n" and m" given by (4.2.1) n" = n' + n t t t and (4.2.2) m'^' = (n^ m' + n^ mj.)/(n' + n^) . In order to study the limiting values of n' and m' under stat t tionary conditions, we have to characterize the posterior and predictive tlistributions after t periods of time have elapsed. Since the limiting results under nonstat ionary means will be based on a fixed sample size

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87 each period, we will make the same assumption for the stationary limiting results, that is n = n, Vt. In period one, for a process that has a normal density function with unknown parameter y , i .e., f (x | p) , the natural conjugate prior is normal with mean m' and variance a^/n' , i. e., f (p, |m' ,a'^/n ' ) . If a sample of size n from a normal process yields the sufficient statistics m and n, then the posterior and predictive distributions at the end of period one are given by (4.2.3) f|^ [pj(n;mj + nmj)/(n|+ n),a2/(n|+ n) ] = f j^(p Jm!||,o2/n'p or = fj;j(M2|m',a2/n') , and (4.2.4) fj^(x-^|m^, a2(l + n!^)/n!j;) , respectively. In period two, if a sample is taken from a normal process that yields the sufficient statistics mÂ„ and n then the posterior and predictive distributions at the end of the period are given by, (4.2.5) f;; [fi2l[n{ni{ + n(mj+ m^) ] / in[+ 2n) . a2/(n| + 2n)] = f;;(p2|m2, ^^/^'^ or and = f^(M2|m^, o^/n^) , (4.2.6) f^^(xjm|;, o2(l + np/n'p respectively.

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88 After t samples are taken the posterior and predictive distributions are given by (4.2.7) f'; (il |m",a2/n") N t' t t and (4.2.8) f^ (xjm'^,a2(l + n'p/n'p , where t (4.2.9) m'^ = Cn| in! + n Z m.)/(n: + t n) i=l ^ and (4.2.10) n|.' = n| + t n . We pointed out in Chapter Three that if the data generating process is lognormal with unkno\,m parameter \a , then the natural conjugate prior is normal and the predictive distribution is lognormal. For this case after t samples are taken that yield sufficient statistics n (ni^,n), (m^.n), ... (m^ ,n) , (where m^. = [ Z lnx^_.]/n), the posterior i=l and predictive distributions are normal and lognormal, respectively, with parameters m'^ and n" as defined in (4.2.7 4.2.10). The mean and variance of the predictive distribution when the data generating process is normal are given by (4.2.11) E(x^) = ra" and (4.2.12) V(x^) a^in'^ + l)/n" . On the other hand, the mean and variance of the predictive distribution for the lognormal process are given by

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89 (4.2.13) E(x ) = exp[m" + o-(l + n")/2n"] and (4.2.14) Var(x ) = exp (m") /w(w-l) , where w = exp[a^(l+ n")/n"] . The mean and variance of the posterior distribution of y for the normal and lognormal cases are given by (4.2.15) E(p^) = m'^ and (4.2.16) Var(p^) = a'^/n'^ . Since n is a positive integer for all t, n' ( = n' + (t-l)n) increases without bound as t increases, so that the variance of the posterior distribution of p for the normal and lognormal cases approaches zero as t increases. Intuitively, in the stationary case, the distribution of the unknown parameter becomes tighter as more information is obtained. As expected, when the data generating process is normal the variance of the predictive distribution approaches the process variance, a'^> as t increases, i.e.. (4.2.17) Jim {a2(l + np /np = lim {(a^/n^ + o2} = a^ , since the uncertainty about p is eliminated as t approaches infinity. In any given period t, m" is a weighted average of the prior mean at period one, m! , and of all past sample means, m , m , ... , m .

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90 All sample means up to period t are given the same weight, n, in the determination of the posterior mean, m" ; in other words recent observations receive the same weight as not-so-recent ones. Moreover, in any period t, the prior information contained in the parameter m' has a weight n'/(n' + tn) , which decreases as t increases. The variance of the predictive distribution for the lognormal case depends on the parameters n" and m" [see (4.2.14)]. For t very large, the term /w(w-l) approaches a constant since w = [o^(l + n")/n"] approaches exp(o^) . As t increases the changes in the predictive variance are produced solely by changes in m" since /w(w-l) is convergent. The mean of the predictive distributions for the lognormal case also depends on n" and m", [see (4.2.13)]. Since the variance a' approaches zero as t increases, the posterior mean m" approaches the unknown population mean P of In x . That is (4.2.18) E(x ) -^ exp[y + {a'^H)]. Suppose now we assume as in Chapter Three that the process generating the observations undergoes a mean shift between successive periods. In particular, values of the parameter for successive time periods are related as (4.2.19) P^+l " ""t "^ ^t+1' e '^ N(u,[o2/n^]) We pointed out in Section 3.3 that the prior distribution of V in any given period t+1 is given by. (^-2.20) f'0',^J-;^i. o2/n;^^).

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91 where (A. 2. 21) m' ,, = ra" + u t+1 t and (4.2.22) n;^^= [n-'n^/(n''+n^)l
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92 p , Even though the observations in the first period yield yet further information concerning y , the random shock at the end of the period is strong enough to imply that there is less information about y^ ^^ the beginning of the second period than there was about p at the benginning of the first period. On the other hand, if n| is less than n^, then the information obtained each period "overrides" the uncertainty caused by the random shock, in a sense, and there is more information about ^2 at the beginning of the second period than there was about y at the beginning of the first period. To investigate the behavior of the sequence (m') assume as before that the sample size in each period is n, In addition, to obtain a simpler expression for comparisons with the stationary case, assume that 1) the mean of the distribution of the random shock is zero, i.e., u=0 and 2) at the beginning of the first period the model is already in steady state form in the sense that n' = n , so that the sequence of variances (o^/n') will be a constant sequence (once the process reaches the limit n it remains there). Based on the assumptions, from (4.2.21) and (A, 2. 2), m'^^ can be expressed in the form t+1 1 (4.2,24) m^^^ = qm' + (l-q)m^. rhe result can be motivated as follows: it is assumed that n =n, and nl= n which Implies n'= n ; tlierefore it follows that the posterior mean can be expressed By ^'c+l" ""t " ^"t"'t"^ "t'"t^''^"t"^ "t^ " ^"h^'t"'" ""'t^^^^h'^ "Â• = (nj /(n^+ n)lm^ + In Defining q as in (4.2.25) is follows that m', , = qm' + (l-q)m , = I", /("l"^ n)lm^ + ln/(n^+ n)ra^.

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93 where (4.2.25) q = iij^/Cnj + n). Note tluit <: q < 1 , When we successively apply (4,2,24), m' becomes a function of ni' (the initial mean), m. (the sample means) and of q. The prior mean of the unknown parameter p, after t periods of time have elapsed can be written as (4.2.26) , t , ^ ,, , ^"^ i ""t+l = 'I '"t "Â•" ^^""^^ ^ "^ Â™t-i Â• i=0 Unlike the stationary case, the sequence (m') does not have a limit. Tlie prior mean at the beginning of any period, under nonstationarity, can be expressed as the sum of the initial mean, m', discounted by a factor q and an exponentially weighted sum of the observed t 2 1 sample means. Since q is a constant less than one, q < ,,, < q < q , Thus as we move into the future the initial prior mean has less weight in the determination of the prior mean m'. From the exponentially weighted sum of sample means we note that recent observations are weighted more heavily than not so recent ones. The impact of a particular sample mean on future values of the prior distribution of \i decreases as t increases. Under the same assumptions that we used to present the limiting results of n' and m ' , the mean and variance of the normal predictive distribution when the data generating process is normal are given by (4.2.27) E(x^) = m'^' [as defined in (4.2.26)],

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94 and (4.2.28) Var(x ) = (72(1 + n ) /n respectively. Similarly when the data generating process is lognormal the mean and variance of the lognormal predictive distributions are given by (4.2.29) E(X|.) = exp [m'^.' + 0^(1 + n^)2n^], and (4.2.30) Var(x ) = exp(m") /^"(w^^ , where w = exp [a^Cl + n ) /n ] . 'L The additional uncertainty involving the shifts in the parameter p affects the predictive distribution of the random variable depending on how the initial parameter n' relates to the limiting value n . If 1 L" n' is larger than n , the variance of the predictive distribution for 1 ^ normal processes, 0^(1 + n'')/n", increases as t increases. Again there is initially a great amount of information concerning x. The information obtained each period from the sample ia not strong enough to override the uncertainty caused by the random shock. There is not a similar effect in the variance of the predictive distribution for lognormal processes since it depends on both parameters m" and n" . The expected value of the predictive distribution for normal cases does not have a bound. It is influenced heavily by the most recent sample means. The expected value of the predictive distribution for lognormal cases also depends on both parameters m" and n" . K t t

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95 A. 2. 2 Limiting Behavior of ra' n' v' and d' When Both Parameters \i an d o^ Are U nkno wn The most involved of the normal or lognormal cases is, quite naturally, that in which neither p nor o^ is known. It is clear that we shall have to assign (fi,d^) a bivariate prior density function. The natural conjugate prior density function of (p,a^) is the normal-gamma-2 with parameters m', v', n', d'. If the mean and variance of the data generating process are stationary and sample information arrives each period then each posterior can be thought of as a prior with respect to the following sample. In general if we assume that a sample of size nj. is employed every time a sample is taken, and the sample yields sufficient statistics m , n , v and d , and if we assume that the parameters do not change then in any given period t the bivariate distribution of i\i,a^) is normal-gamma-2 with parameters iii" , n" , v" and d" given by (4.2.31) m'^ = (nm' + n^m^)/(n^ + n^) , (4.2.32) n'j! = (n+ n^) , and (4.2.23) v" = (d'v' + n'm'+ d v + n m^-n"m"2) / (d ' + n ), t 'tt tt tt ttttt t (4.2.34) d'^ = d^ + n^.. To study tlie limiting behavior of m' v' , n' and d' under stationary and nonstat ionary conditions we will make the assumptions tliat n =n Vt and that d = d Vt . After t samples are taken the sufficient sta-

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96 tistics (m , V , n, d) , (m , v , n, d) ... (m , v^, n, d) are available. The characLerization and consequently the limiting behavior of m' and n' are identical to the ones presented for the case when p is the only unknown parameter, [see equations (4.2.9) and (4.2.10) for the stationary conditions and (4.2.23) and (4.2.26) for nonstationary conditions]. The characterization of the parameter d' is rather simple. Under stationarity and nonstationarity the parameter d' is equal to the parameter d". After t periods of time the following relation holds, (4.2.35) d" = d' + tn. t t The limiting value of the parameter d" approaches infinity as t approaches infinity. The characterization of the parameter v" is more involved. Before considering the characterization of v" a transformation of the original expression is to be presented. Expression (4.2.33) could be rewritten as (n'm' + nm )2 d'v' + dv n'm'2 + nm^(n'+ n) [ Â— "^ / , , Jy] (4.2. 3fa) ' 'I . ^t (n'2m'2+n^m2+2n'nm'm ) Â°^ 1 .-> _L. 2 r t t t t t t ' ., , , . n m^ + nm-^ I Â— ; Â— Â— Â— 1 d'v'+dv tt t n'+n ^ (4.2.37) v" = "^ "^ ^ t d" d" t t Combining terms and simplifying (4.2.37) becomes

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97 n'n (ni'"^ + m^ 2m 'm ) II,, r t t t t t d'v' + dv [Â— p--Â— Â— -;^-^-] (4.2.38) v"=-^-t_ + "t -^ " ^ '^t d" t or nn'(m' m )2 (4.2.39) v" = [d'v' + dv + Â—5 ^f-r---1 / (d' + n) ttt n'+nt It can be noted that given o^ , (n! + n) (4.2,40) V(m ) = E m m']= Â— Â—rt ' t t n'n t o2 ; so t ha t n'n (m m ' ) ^ (4.2.41) E [ ^ n'% ~n^~^ = Â°^' [see Raiffa and Schlaifer (1961)]. Thus assuming that v' and v are obtained as unbiased estimators of o^, unbiasedness In v" Is preserved ' t ' by the Inclusion of the third term in the numerator of v". v' will t t only be unbiased if it was based on a noninformative prior at time t=0. Otherwise it is biased by prior information. Now consider the characterization of v" as defined in (4.2.39) t In period one the posterior value of v is given by d'v' dv n'n (m iii ' ) (4.2.42) vV = ,4-7 Â— + TV~+ 1 d' + n d' + n (n'+ n) (d|+ n) ' in period two the (josterior value of v is given by

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98 d'vj dv n'n (m^ mj)'n'n (mÂ„ m') "^ (n|+ n)(d]+ n) "" (n^ + n) ' ^""^ In period three tlie posterior value of v is given by (4.2.44) d'v' dv dv^ dv "^3 " 7dM^ny(d]+2^(Trp-Ti7)" ''" (d '+n) (d ' +2T)yTd''T3rO "^ ("d ' +27^)Td' +170 '^(d"'!-!'^^ ^ n'n (m,m')^ n'n (m, m')^ n'n(m -m')^ , 1 1 1 , I I Z J J J } ( (n|+n)(d|+a)(d|+2n)(d|+3n) ^ (n'+n) (d|+2n) (d|+3n) (n^+n) (d]^+3n) In any given period t the value of v" depends on the stationarity condition. Let v" be the sum of two terms (a) and (b) as defined in (4.2.43) and (4.2.44). Term (a) does not include parameters that depend on the nonstationarity assumption but term (b) does. It has been pointed out many times before that n' is affected by the nonstationarity assumption. To study the limiting behavior of v" under conditions of nonstationarity it is assumed as in a previous example that n'= n and consequently n' = n Vt. Define P = n n/(n + n) . Based on the ' ^ t L L L assumptions and the definition of P, expressions (4.2.42) and (4.2.43) will be written as

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99 and
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100 distribution of a^ : v'd' 232 v'd' d'jv^ (4.2.49) f ,,(l/a21v',d') = ^ ^&^ J I 2 J y_/ I Â— Â—^ r(d'/2) d'v' ^ _ 1 (4.2.50)
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101 Define and (4.2.56) d" = d' + t(n-l) (4.2.57) v" = E (n-l)v. + d'v7[d' + t(n-l)l ^ ' i=l ^ The posterior distribution of 5^ could be rewritten as v'; ,d" d" (t) t ,, .Â„ t (4.2,58) f"(a2|v" ,,d") -T^^Â— V' -d" -Â„1 d" v'; , 20-^ r_(t) 1]^ r t (t) tTP ' ^ 2 (t)' t' r(d'72) Now lets look at the limiting behavior of v'l , t (n-l)v. + d'v' lira v'! X = lini E Â— rv Â— ; ; :-,(t) . , d' + t(n-l) t->Â«> tx" 1=1 t (n-l)v. lira E ^ . . t(n-l) From sampling theory it is known that if the sample variance t is defined to be v = l (x .m )2/(n-l) then E(v lu ,0"^) = o^ and t . , ti t t' t 1=1 V(v 111 ,0^) = 2a'V(n-]). Assuming that v .., v are i.i.d. then t t , E( T. v./t) = oand V( E v./t) = Var(v.)/t2 = [2o'+/t(n-l) -> 0. i=l ^ i=l ^ ^ . t-^ Therefore 1 im v = o"^ w.p.l. t ^ t '^''^

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]02 lim v" = lini I v./t (t) . , 1 t-J-co t->'^n 1=1 = o^ w. p. 1 . We have shown that there is a sequence {v, , ) as defined in (4.2.57) which converges to o^. Moreover the Bayesian can observe (v, ,] therefore by observing {v, } he comes to know 5^. 2, In the limit since he knows n^, his limiting posterior distribution of a^ must be degenerate at lim v, , = o^, 3, Raiffa and Schlaifer (1961) show that the mean of the gamma-2 posterior distribution of (l/o^) is equal to the inverse of the posterior estimate of the variance as defined by (4.2.54) E'^(l/a2) = l/v'^ . 4, Therefore by (2) and (3), it must be true that (4.2,60) lim v'^ = lim v = o2 w,p.l , Observe that the argument presented before applies to both stationary and nonstationary cases. Savage (1971) summarizes informally the argument we have presented.

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103 4 . 3 Predi c tion Interv al s for Normal, St uden t, Lo gnormal and LogS tudent Distributions Bayesian analysis is generally concerned with the past only insofar as it relates to the present and future; interest is with the current situation and how it relates to what might happen rather than with what did happen. Above all, it is concerned with creating a meaningful view of the future in the minds of people v;ho make decisions. The Bayesian metliods, however, include people explicitly the person responsible for the analysis and all the people concerned with using the output information and supplying information relevant to the resulting actions. Apart from the fact that classical analyses often ignore external information and apart from the fact that the statistical criterion is usually far from reflecting the decision loss function, the analysis often neglects the people who will communicate with each other and the model. People have sources of information quite beyond the data; for example, they may know perfectly well that a competing product is being introduced, that a new tliechnology has been developed, or that the President is planning to sign a new legislation that will affect the marketing of tlieir product. The effects of such events can often be well foreseen in a qualitative or subjective sense, but it may nevertheless be difficult to be expressed and require probability distributions to describe the uncertainty surrounding them. It is necessary that people can communicate tlieir Information to the metliod and that the method clearly communicates tlie uncertain information in such a way that it is readily interpreted and used by decision makers. The nonstationary model that we

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104 developed in Chapter Three for normal and lognormal processes incorporates prior distrilmtions on the unknown parameters to reflect the decision maker's information. In a Bayesian analysis, the information coming from the data is contained in the posterior distribution of the unknown parameter. One way to partially summarize the information contained in the posterior distribution is to quote one or more intervals which contain stated amounts of probability. For the classical statistician, the information coming from the data is contained in the sampling distribution. He can summarize the information in the sampling distribution quoting intervals with confidence coefficient Y. Suppose that x , . . . ,x form a random sample from a distribution which involves a parameter 6 whose value is unknown. Suppose also that two statistics T (x, , . . . , x ) and T,-,(x,, ... , x ) can be found such that, no matter what the value of 6 may be (4.3.1) Pr[T^(x^, ..., x^) < e < T2(xj^, ... , x^)|e] = y, where y is a fixed probability (0< Y <1) Â• If the observed values of T^ (x , ..., x^) and T2(x-,, ..., x^) are a and b, then it is said that the interval (a,b) is a confidence interval for 9 with confidence coefficient Y, or, in other words, that the interval (a,b) contains with confidence Y. The uncertainty pertains to the interval, and not to 6. It is not correct to state that lies in the interval (a,b) with probability )' . Before the values of the statistics T (x, , ... , x ) and T^Cx^, ... , x ) are observed, those statistics are random variables,

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105 It follows therefore from (A. 3.1) that y will lie in the random interval having end points T, (x , ... , x ) and T (x, , ... , x ) with pro1 1 n 2 1 n bability y. After the specific values T (x , ... , x ) = a and ^1 n TÂ„(x, , ... , x ) = b have been observed, it is not possible to assign a probability to the event that lies in the specific interval (a,b) without regarding 6 as a random variable which itself lias a probability distribution. In other words, it is necessary first to assign a prior distribution to 6 and then to use the resulting posterior distribution to calculate the probability that 6 lies in the interval (a,b). Rather than assigning a prior distribution to the parameter 6, classical statisticians have preffered to state that there is confidence y. rather than probability y, that lies in the interval (a,b). To a classicist, any given confidence interval statement is either correct (in which case the probability that it is correct is 1.0) or incorrect (in which case the probability tliat it is incorrect is 0.0) . That is, a confidence interval is one type of interval estimate that has the feature that in repeated sampling a known proportion (for instance, 95%) of the intervals computed by a given method will include the population parameter. This concept has a shortcoming since, although the particular sample values that are observed may give the experimenter additional information about whether or not the interval formed from these particular values actually does include 0, there is no way to adjust the confidence coefficient y in the light of this new information. To differentiate betv>;een the two statements, usually the classical interval estimate is called a "confidence interval" and the Bayesian interval

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106 is called a "credible interval". Users of classical intervals tend to interpret them in the subjective sense as probability statements about a random variable 9 despite the classical statistician's emphasis on the frequency interpretation. Pratt (1965) has observed that people should not be blamed for this misinterpretation, since the correct interpretation ([a,b] is the interval which, before the observations are obtained, had probability Y of covering 6) is simply not relevant to people concerned solely vvfith 9 and not with the observations vjhose only role is to furnish information about 9. The classical approach often requires that Y, the probability associated witfi the interval estimate, be chosen in advance of sampling. The Bayesian may wish to look at intervals for several different values of Y (not necessarily chosen in advance) . A rather interesting situation arises when an interval is needed to show a range within which most of the distribution lies. In searching for ways to summarize the information in the posterior distribution P(S|x), wliere 9 is the unknown parameter and x is the vector of observations, it is to be noted that, although the interval over which the posterior density is nonzero may extend over infinite ranges in the parameter space, nevertheless over a substantial part of the parameter space the density may be negligible. Thus it may be possible to construct a relatively small interval which contains most of the probability or to construct a number of intervals wliicii contain various stated proportions of the total prol)abil i ty . There are an infinite number of ways in which these intervals can be constructed.

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107 In some applications, two properties are desirable for such intervals: 1) the probability density of every point inside the interval is at least as large as that of any point outside it, and 2) for a given probability content the interval should be as short as possible . Intervals v;hich have these properties have been called highest posterior density (H.P.D.) intervals. The normal, lognormal, Student and logStudent will permit H.P.D. intervals. Moreover for these distributions, as for any unimodal distribution, the H.P.D. interval of content y is unique. Throughout the discussion in the previous paragraphs we assumed that there was only one unknown parameter. If we are referring to a vector of the unknown parameters, i.e., 6= (0i,6o), all that can be known about 6 is contained in the joint posterior bivariate distribution. Mathematically speaking, therefore, the problem of making inferences about y is solved as soon as the posterior distribution is written. As soon as we consider more than one unknown parameter we refer to highest posterior density (H.P.D.) regions instead of H.P.D. intervals. As with H.P.D. intervals, the region should be such that the probability density of every point inside it is at least as large as that of any point outside it or the region should be such that for a given probability content, it occupies the smallest volume Sometimes in order to have the smallest total width one must

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108 in the parameter space. We em[)hasized in Cliai)ter Three that one of the purposes of prediction is often to provide some estimate, either point or interval, for future observations of an experiment F based on the results obtained from an informative experin\ent E. In other words, in addition to being interested in the posterior distribution of the unknown parameters we are interested in the distribution of further samples or observations. For instance, it is sometimes of interest to obtain a value, arrived at by life testing, that with high probability will be less than the life length of a particular component that is to be used in a system. Or, on the basis of annual profits in previous years, a firm is interested in having an estimate, in interval form, of the profits for the coming year. These are examples of statistical inference problems called prediction intervals or |i-expectation tolerance intervals. The problem can be stated more formally as follows. [See Aitchison and Schulthorpe (1965) and Fraser and Guttman (1956).] Suppose an informative experiment has been performed. A random sample x,, X2, ... , Xj^ is taken from a distribution that belongs to the class of density functions [p (Â•|e):6t;0], E ' say f(x|6). Also assume that there is a future experiment F, which consists of taking a random sample Y, for which a prediction of some sort is required and that the possible probabilistic descriptions of F form the class of density functions [p (Â• | 6 ) :(;)fc ] . The densities describing E and F are conditioned by the same parameter vector. It is through this connection between E and F that E provides information about F.

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109 Although E and F are connected by 9 , It iq assumed that for given Q they are statistically independent. On the basis of the sample x, , ... , x we wish to make a prediction about Y, usually in the form of an Interval or region tiiat we are confident will contain the outcome of Y. That is, if L and U are functions of x, , ... , x , then 1' ' n (4.3.2) Pr( 1, < Y < U ) = g, or equivalently (4.3.3) E { /' f(y|e) dy} = 3. L Aitchison and Sculthorpe (1965) classify the prediction problem in two categories, first a prediction is required for only one performance of F and second a series of replications of V is to be conducted and then the prediction region, R, is to be used for each replication. Although there could be more than one replication in a single time period and one can still get prediction intervals from future replications from what we know about m' and e, we are restricting ourselves to single replications. In other words, each time that we find the predictive distribution we will be concerned with one, future experiment F. Faced with case one a Bayesian would proceed to obtain f(y|x), the posterior distribution of y given x. As we pointed out in Chapter Three, from a prior density f(6) on the posterior density f(0|x) is obtained in the usual way Â£ind this is convened into f(y|x) through the relation (4.3.4) f(y|x) = / f^(y|e) f(elx) de r

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IJO f(y|x.) is called the predictive disti-ibution of y. Most of tlie liter.iture on prediction intervals is concerned with solving for prediction intervals of a particular type or solving for intervals for a particular distribution. For instance, Thatcher (1964) found prediction limits for binomial variables which do not depend on any assumptions about the unknown proportion in the population. Hahn (1969) considers prediction regions for k future Y ol^servations when sampling from a normal distribution. Shah (1969) and Nelson (1970) present a method for obtaining prediction intervals for a Poisson variable and generate prediction limits for the numl)cr of failures in one time interval by observing the failures in the other time interval, prt)vided both observations are subject to the same Poisson law. Faulkenberry (197 3) obtains a prediction interval for a random variable Y based on the conditional distribution of y given a sufficient statistic for the conditioning parameter. Aitchison (1966) considers the construction of linear utility tolerance intervals which do take into account how far inside or outside the interval a future observation y happens to fall. From a Rayesian viewpoint, it is found that expectedcover and linear utility intervals can be regarded as equivalent through a simple relation between the expected cover and the relative cost ratio. For the frequentist approach, it is first shown that linear-utility intervals can be simply constructed for the normal and gamma distributions. Comparison of these with expec ted-cover intervals shows that, while there is no complete identity, there is an equivalence in a "large sample" sense.

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HI Prediction intervals for future observations in life testing situations have been derived also by Hewitt (1968), Nelson (1968), and Lawless (1971, 1972) through the use of expected-cover tolerance regions. Dunsmore (1974) gives a Bayesian approach to such situations and uses the concept of the Bayesian predictive distribution. He considers both the exponential and the two-parameter exponential distributions. As was pointed out in Chapter Two we intend to use the same approach for the construction of prediction intervals for the normal, lognormal. Student and logStudent distributions under conditions of nonstationary shift parameters. If the prior distribution is natural conjugate to the process then the predictive distribution for normal processes is normal when U is unknown and a^ is known and is Student when \i and o^ are both unknown. The determination of prediction intervals in general and H.P.D. intervals in particular is easy due to the characteristics of both distributions. The normal and Student distributions are similar in the sense that they are unimodal, symmetric, bell shaped, and asymptotic, extending from minus infinity to plus infinity. Graphically, the standardized Student distribution is flatter than the normal distribution, with a larger portion of the area under the curve located in the tails of the distribution. This implies that one must proceed a greater distance along the number line away from the mean under a standardized Student distribut ion to include any given percentage of the area under the curve than would be the case for the standardized normal distribution. Since both distributions are symmetric, to construct H.P.D. inter-

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112 vals of Y content it suffices to take the area between the lower limit of the interval and thtPiean to be equal to the area between tlie mean and the upper limit of the interval. If we let a be the lower limit, b be the upper limit and c be the mean the condition could be written as, (4.3.5) I"" f(x|y) dx = (y/2) = /^ f(x|y) dx . a a Since the distributions are symmetric, the length of the Interval between the lov;er limit and the mean is equal to the length between the mean and the upper limit. To obtain the probabilities needed to determine the limits of the interval we use a table of the probability integral of the normal and Student curve depending on the assumptions of tlie problem. Prediction intervals of content y take the form (A. 3.6) RCx) t K, Std. Dev. (x) , 1-Y wliere K refers to the number of standard deviations one must proceed 1-Y in one direction from thi' mean in order to encompass (y/2) percent of the area under the curve. For the case wht-n p is the unknown parameter and the data generating process is normal, assume that after t periods we have a posterior distribution f"(ii ) which is normal with mean m" and varit ^t t ance n^/n". The predictive distribution at the end of period t was sliown in equ.ition (AI.12) to be normal witli mean, m", and variance, o'fl + (1/ri")]. For tiiis case the prediction intervals of content y

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113 take the form (A. 3. 7) m" . tC [o2(l + [i/n'']))^/^ t l-y I For the case when both [i and o^ are the unknovvai parameters and the data generating process is normal, assume that after t periods we have a posterior distribution f"(ij ,0"^) which is normal-gamnia-2 with parameters m" , n", v" and d" . The predictive distribution at the end of period t t t t t was shown in equation (AI.33) to be Student with mean, m" , and variance, [v"(n'' + ])/n''J ld'7(d"-2)]. For this case the prediction intervals of content y take the form (4.3.8) m'^ J K^_ {[v-jlCn'^ + l)/n'^'] d" / (d"-2)]^^^\ The predictive distribution for lognormal processes is lognormal when y is unknown and ois known and is logStudent when p and o are both unknown. The construction of prediction intervals in general and H.P.D. intervals in particular becomes difficult for the lognormal and the logStudent predictive distributions since these distributions are asymmetric. In Appendix III we provide an algorithm to construct the H.P.D. intervals when the predictive distributions are asymmetric. In any given period t the user only has to provide the current values of the parameters of the predictive distribution, i.e., m" and n2(l+n")/n" t t t for the lognormal case and m" , v", n" , d'' for the logStudent case. It is shown in Appendix III that the algorithm finds tlie highest posterior density intervals in very few iterations. It took about 15 iterations

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114 to find the intervals in the examples that were considered. In Cliapter Tliiee we pointed out that, under nonstationary conditions, if the data generating process is normal for the case when p is the unknown parameter and for the case when p and g^ ^j-g t^i^g unknown parameters the predictive distribution changes in mean and variance between consecutive time periods. The E , , (x ) is always changing depending on the stochastic change of the mean p . I'hus it is not possible to establisli how the H.P.D. interval for this predictive distribution compares with the stationary H.P.D. interval under the same assumptions. However, in the case of nonstationarity v\7itli no drift, i.e., u=0 for a given posterior distribution of p at time t, the only difference between the predictive distribution of x , under stationarity and the predictive distril)Lition of x under nonstationarity is the variance t+1 term. As expected, the variance of the predictive distribution is larger when p is nonstationary. For normal and Student processes the H.P.D. interval will be wider for a given content y when p is nonstationary than when p is stationary. A comparison of stationary versus nonstationary results when the data generating process is iognormal shows that as in the normal case, the nonstationarity condition causes the prediction intervals to be larger under the nonstationary conditions than under stationary conditions for both parameter uncertainty cases. A rather different approach to the prediction problem, termed the Certainty F.quivalent (CK) approach, is considered by Holt Â£t . a^l . (1960) and Tlieil (196A) among others. Suppose, as in the classical school, that the parameter ii of a normal distribution is fixed rather than random.

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115 but that the decision maker does not know this fixed value and estimates it by means of some statistical procedure; or consider the case where p is random and its expectation is E(y) , but tlie decision maker does not know E(p) and estimates it. In the CE approach the decision maker uses the estimates of the uncertain parameters in place of the relevant true values, i.e., they are treated as if they were the actual values of the parameters. According to the method, the point estimate constitutes a certainty equivalent for complete knowledge of the distribution function. That is, if the distribution of x is f(x|ft), where is an unknown parameter or vector of parameters, then an estimate e for the unknown parameter constitutes a certainty equivalent and f(x|e) is considered to represent full knt)wledge of the distribution f(x|o). The decision maker then bases all his probability statements and decision choices on the distribution f (x I o) . Theil (196A), Brown (1976), Barry (1974) and Barry et. al. (1977) show that the CE approach can lead to inappropiate decisions since it does not reflect uncertainty in as is done in the use of predictive distributions. However this approach allows the decision maker to make the probability statements of most direct interest to him without using confidence Interval terms. Thus this approach, CE. would seem preferable in some respects to a classical confidence interval approach. Since there is the problem that the true parameters may deviate from the estimates, a problem that is variously referred to as estimation risk or parameter uncertainty, much effort has been devoted to the task of improving the estimate that is

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116 made of the vector of parameters 9. The Bayesian, on the otlier hand, asseses a probability distribution over the range of possible values q can assume. In general, no one point can fully capture the information contained in this distribution except in the special case where it is concentrated about 0, so the Bayesian methodology provides an approach which uses as much information as possible. In the case where the prior beliefs of the decision maker, with regard to the unknown parameters, have convenient representations, i.e., mathematically tractable forms, the Bayesian approach has been shown to perform better than the CE approaiJi. To the extent that a CE distribution misrepresents the decision makers predictive, the CE approach can lead to inappropiate decisions [see Brown (1976)]. In conclusion, since the CE approach does not include the parameter uncertainty it understates the uncertainty faced by the decision maker and could produce predictive distributions that are misleading. Since the CE approach does not consider parameter uncertainty, it yields prediction intervals that overstate the content probability or (equivalently) understate their risk. Thus the CE approach discards information, i.e., the distribution of 6, and then gives interval estimates that appear more informative than the Bayesian highest posterior density intervals. In Chapter Five we are going to show some examples of this condition when vje present applications of the results from Chapters Three and Four to Cost-Volume-Prof it Analysis and life testing models.

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117 4 . A Conclusion In this c:hapter we discuss the limiting behavior of tlie parameters m', v', n' and d' of the prior and predictive distributions for the normal and lognormal data generating processes. In addition we discuss the implications of these limiting results for the inferences and decisions based on the posterior and predictive distributions. The asymptotic behavior of the model has important implications for the decision maker. An implication of the stationary Bayesian model for normal and lognormal processes is that as additional observations are collected parameter uncertainty is reduced and (in the limit) eliminated altogether, In contrast, for the nonstationary model considered in this dissertation the following inferential results are obtained: 1. for the case of lognormal or 'normal model, a particular form of stochastic parameter variation implies a treatment of data involving the use of all observations in a differential weighting scheme; and 2. random parameter variation produces important differences in the limiting Isehavior of the prior and predictive distributions since under nonstationarity the limiting values of the parameters of the posterior and predictive distributions cannot be determined clearly. The protilem of constructing prediction intervals for normal. Student, lognormal and logStudent distributions is considered in this' chapter , It is pointed out that it is easy to construct these intervals

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118 for the normal and Student distributions but that it is rather difficult for the lognornial and logStudent distributions. An algorithm is presented that efficiently compute Bayesian prediction intervals for lognormal and logStudent distributions.

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CHAPTER FIVE NONSTATIONAKTTY TN CVP AND STATTSTICAT, LIFE ANALYSIS 5 . 1 Introductl on In Chapter Four v;e pointed out that one objective of this dissertation is to develop Bayesian prediction intervals for future observations that come from normal and lognorr.ial data generating processes under conditions of nonstationary means. In particular we stressed the importance of tiighe'St posterior density intervals as a mean to convey to the decision maker wli.it he is entitled to believe about the predictive distribution of the variable of interest. This kind of analysis is partiiularly useful in tlie area of Cost-Volume-Profit (CVP) Analysis (see Dickinson (1474), Hilliard and Leitch (1975) and Kaplan (1977) among others) and in tlie area of Statistical Life Analysis (see Folk and Browne (1975), Jones (1971) and Dunsmore (1974) among others) since the application of the lognormal distribution is not only based on empirical observations, but in some cases is supported by theoretical arguments. The lognormal distribution has been found to be a serious competitor to the Weibull distribution in representing lifetime distributions for manufactured products. In Section 5.2 we discuss the afiplication of the results of Chapters Three and Four concerning nonsta tionar i ty to the area of CVP analysis, i'hf proMem of CVP analysis will be considered from a Bayesian viewpoint, and inferences under tlie special case of nonstat ionarity developed in (lliapler Tlux'-c v; i 1 1 he discussed. Also the Bayesian results 119

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120 under nonstationar i ty will be compai-ed with some alternative approaches suggested in the accounting literature. In Section 5.3 we incorporate our results into Che theory of Statistical Life Analysis. Practical implications of our results for the reliability problems are discussed vjith emphasis on the predictive distribution of the random variable. In Section 5.4 we present the conclusions of the chapter. 5 . 2 Nonstationarity in Cost-V olume-Prof it Analysis 5.2.1 Existing Analysis The scope of CVP analysis ranges from determination of the optimal output level for a single-product department to the determination of optimal output mix for a large multi-product firm. All these decisions rely on simple relationsliips between changes in revenues and costs and changes in output levels or mixes. All CVP analyses are characterized by their emphasis on cost and revenue behavior over various ranges of output levels and mixes. The applicability of probabilistic models for this analysis has been claimed because of the realism of such models. That is, an inherent aspect of any management decision-making situation is the presence of uncertainty concerning one or more of the relevant factors; for exanijile, tlie entire notion of forecasting the value of some variable in the future is based on the fact that there is uncertainty concerning that variable. The ideal model is one that gives a probability distribution of tiie criterion variables. Like profit, that fully recognizes tlie uncertainty faced by the firm and incorporates all available information. Ihe I'ealism of such a model is dependent on assumptions about

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121 the input variables ami rigoruiis mettiodoiogy in obtaining the output distribution. In Chapter I'wo we surveyed some of the relevant literature related to the development of CVP analysis under uncertainty. As was pointed out in that survey, most of the papers reflect how the people that liave studied CVF analysis have neglected one potentially important source of uncertainty to the manager, namely the problem of parameter uncertainty. Classical methods used in CVP analysis generate correct confidence interval estimates only on those occasions where the manaj^^er has no knowledge witli respect to the variable he is attempting to estimate. Such a situation seldom, if ever, occurs. Bayesian methods explicitly treat judgmental information and take the position that any estimate generated should reflect all the information at the manager's disposal. This is reflected by the assignment of a prior distribution, which is used in conjunction with observed sample evidence to form a posterior distribution. Dickinson (197A) addressed the problem of CVP analysis under uncertainty by examining the reliability of using sample means and the unbiased sample variance to estimate the means and variances of the past distributions of sales demand. As pointed out in Chapter Two his paper illustrates the limitation of non Bayesian CVP analysis of not being able to obtain the probability statements of most interest to ttie manager. The Bayesian appr(j;ich provides a general procedure of describing and analy^.ing any suc-h situation without tlie appeal to ad hoc procedures or ingenious tric:ks [see Lindley (1972)], especially through the use of the

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122 predictive distribution. Barry, Velez and Welch (1977) have recently applied a predictive Bayesian model to CVP analysis, explicitly allowing for parameter uncertainty. An implication of such a model is that as additional observations are collected parameter uncertainty is reduced and (in the limit) eliminated altogether. Such an implication is inconsistent with observed real world behavior largely because the conditions under which firms operate typically change across time. A CVP model ideally should include the changing character of the process by allowing for changes in the parametric description of the process through time. Failure to recognize the nonstationary condition may result in misleading inferences. CVP literature has neglected to include this additional source of uncertainty that influences the decision maker's frame of reference for his decision process. In Chapter Three we showed that if tlie presence of nonstationarity is not fully recognized then we can be lead to a serious misinterpretation of the conclusions drawn from a stationary model. 5.2.2 No nstat ionary Bayesian CVP Nodel Assume that a single product firm' has a profit function defined by (5.2.1) Z = Q[P-V] F , where Z = total profits , Q = sales volume in units, P = unit selling price ,

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123 V = unit VÂ£\riable cost cind F = total fixed cost. Thus the firm produces the quantity Q at a fixed cost F, and variable cost VQ . Assume that the only random element in the system is the quantity variable Q. In addition assume that Q is normally distributed with mean u and variance o^ , i.e., f (Q|u,a'^). Later we will consider the cases N ' where other variables are random and also we will modify the analysis to allow for lognormall ty in the distribution of the variables. In general the values of the parameters of the CVP model will be unknown. Consider a manager with a prior distribution over the parameters of the probability model of Q, say f'(e|r) where 6 includes all the unknown parameters and r represents all information known to the manager. In particular assume that if p is the only unknown parameter then the prior distribution is the normal natural conjugate with parameters m' and Q-^/n' or that if y and a^ are both unknown then the manager has a normal-gamma natural conjugate prior witli parameters m' , n', v'; and d'. (See de Finnetti (1962,1965), Murphy and Winkler (1970), Savage (1971), Stael von Holstein (1970a, 1970b) and Winkler (1967a, 1967b, 1969, 1971) for a discussion of evaluation of probability assessors and assessments.) A formal Bayesian analysis articulates the evidence of a sample, say (I , Q.;, , ... , Q , vjitli evidence other than that of the sample, in the form ot a prior distribution of the parameters to obtain a posterior distribution of tlie unknown parameters. In areas like CVP analysis it is

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124 doubtfLil that the assumption of stationary parameters will hold over long periods of time since variables like quantity sold (Q) , costs and contribution margin (P-V) are affected by economic, political and environmental factors. Thus we are going to assume that the distribution of the random variable, sales, undergoes a gradual mean shift between successive periods of time of the form u , = u + e as defined in ^ *^t+l ^t t+1 (3.3.8). Tlie Bayesian analysis provides a natural method to include the remaining parameter uncertainty in the computation of the predictive distribution. The nonstationarity assumption affects the predictive distribution of the coming period's sales quantity Q. If the process is stationary then the predictive distribution of the random variable Q at the beginning of period t+1 is tlie same as the distribution that we had at tlie end of period t. However if we assume the nonstationary condition and that tlie decision maker is aware of the nonstationarity, then the prior distribution of the parameter at the start of period t+1 has a different mean and variance. Consequently, the predictive distribution changes in mean and variance between consecutives time periods. In other words E (x t+i' t+ is always changing depending on the stochastic change of the shift parameter y(-^j . In the case of nonstationarity with no drift, i.e., u=0, if the distrilnition of sales is normal then, assuming that they started from the same posteriors, the only difference between the predictive distribution of X -, under statlonarity and the predictive distribution of Xf.,, under nonstationarity is the variance term. The parameter n^_i i is smaller when p is uukno\vm and nonstationary than when y is unknown but stationary.

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125 Hence, as expected, the variance of the predictive distribution is larger. The predictive distribution under nonstationari ty may be used to make probability statements about sales quantity or, if desired, profits. To illustrate, suppose that the montlily sales Q , Q , ... of a firm are independent and identical J y tiistributed random variables witli common density function f (Q|y,a2) and tliat the population variance o^ is known to be JOL). Suppose also that, at the beginning of a given period t, the manager has assessed the prior distribution function over the parameter p to be (5.2.2) f;(fiJm',o^7np = f^(p^ 500, 25) . Since o^= 100 and o-/n'= 25, n'= 4. If the manager has available a sample of, say 12, monthly sales with sample mean m =480 then he may compute a posterior distribution of the unknoivm parameter p v;hich will reflect this new information that he has available. Since the normal prior is natural conjugate for sampling from an independent normal process the posterior distribution of the unknov^n parameter y will be (5.2.3) q(u^\vr,o^-/n'p = f;^(pj485, 6.25) . The predictive distribution of Q given the av.iilable information (and uncertainty) about p can be obtained using the posterior distribution of p . T!ie predictive d i str iliut ion of sales at period t is

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126 (5. 2. A) fNC^Im'^;, u2[l+ L/n;'J) = fNCQJ^SS, 106.25). If now we assume Lliat the random shock distribution is (5.2.5) f^Cet-lu, a2/ng) = f^CeJO, 50) then the prior distribution of the unknown parameter jj|at the beginning of period t+1 may be obtained using equations (3.3.10) and (3.3.]1) This new prior distribution is (5.2.6) fi;(p,+ j|m'', a2(n;+ n^)/n^.^) = f'(M,^J^85, 56.25). The predictive distribution under nonstationarity at the beginning of period t+1 is (5.2.7) fj,(Q,.+Jm';, a2[l+ l/n'^^l) = ^^C^+il^^^^ 156.25). It has a higher variance than under stationarity as was pointed out in previous paragraplis. The manager may determine, in any given period t, the predictive distribution of profits from equation (5.2.1). Since the predictive distribution of sales is as defined in (5.2.7) then Lin.' predictive distribution of profits is (5.2.8) fj^(TT^^j|m'; (P-V)-F, o-[l+ l/n^+l](P-V)2). That is, if we suppose that the contribution margin (P-V) is say 8, and that the fixed costs (F) are, say, 1,000, then the predictive distribution of next period's profits is

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127 (5.2.9) f^(n^^j2,880, 10,000). Probability stateiiK'nts are easily obtained using the standard normal distribution tiieory. Observe how this analysis provides the probability statements that the manager needs \v7ithout the necessity of cumbersome phrasing in terms of classical confidence intervals. Lets look now at the same problem but assuming this time that the decision maker knows neither the population variance (o2) nor the population mean (p) . This is the most involved of the univariate normal cases since it requires the assignment of a bivariate prior distribution function to (y,o^). To illustrate, suppose that the manager has ex|)ressed his judgments about (P ,o-) by a normalgamma distribution of the form f'_ (jj^. ,o|m' v' n ' ,d ' ) = f ' (jj ,5-^ | 500 , 25 , 10 , 7) Assume that the manager takes a random sampie of 12 monthly sales, which are assumed to come from a process with unknown mean and variance, and that the sample yields a sample mean (m ) of 480 and a sample variance (v^) of 80. He may compute a posterior distribution of the unknown parameters pj. and o using equations (3.3.17) and (3.3.18). Since the normal-gamma prior is natural conjugate forsampling from an independent normal process, V'jith unknown parameters, the posterior distribution of p and a will be (5.2.10) fN_/^'Â°'l"i't' ^'i'"t' 'Ip = ^^t!,_. ^^'^^1 ^^^' 4784. A, 22, 19)

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128 Under stacionarity the prior distribution at the start of period t+1 is e(iiuil to the posterior distribution at the end of period t. The predictive distribution of Q given the available information (and uncertainty) about p and a^ can be obtained using the posterior distribution of p and d^ . From equations (3. 3. 49) and (3.3.50) we know that the predictive distribution of sales at the beginning of period t is (5.2.11) . f2^(QJm'^, v'^in'^ + l)d;_'/n'j;[d'^'-2]) = fg^,(Qj485, 5590.32). If we now assume tliat the random shock distribution is (5.2.12) fN(et|u, o2/n^^) = fN(it|0, 0^/2), then the manager may obtain the new prior distribution of p and 5^ at the beginning of period t+1 using equations (3.3.25 3.3.28). This new distribution is (5.2.13) ^'N-/f't+l'Â°'l4'' ^t' "t"s/("s+"P' d") = f^:^(Mt+i.5-|485,4784.4,l-83,19) The predictive distribution of sales for the coming period under nonstationary conditions is Student with mean 485 and variance 8,269.28. As expected, the additional uncertainty introduced in the model by the shifting means has caused an increase in the variance of the predictive distribution. If tiie manager does not recognize in liis predic-

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129 tive model the existence of a nonstationary ronditioa lie may draw inferenres from tlie model that are misleading. In any given period t, the manager may determine the predictive distribution of profits from equation (5.2.1). The predictive distribution of profits is (5.2.14) fsT(^+]lÂ™t+l(P-V)-F,(Jt+l/^t+l2)fv' + i(n'^^+ 1) /n^^^ ) [P-V] ^) . That is, if we assume as before that the contribution margin (P-V) is 8, and that the fixed costs (F) are 1,000 then the predictive distribution of next period's profits is fÂ„ (fl 12880, 357,786.25) under stationarity and (5.2.15) fsT*^^t+ll "^^Â°' 529,233.92) under nonstationari ty . Probability statements are easily obtained using Che standard Student distribution tables available in many books. The use of normal distributions in Â•aijplications where the coefficient of variation is large can present many difficulties. The lognormal distribution is in at least one important respect a more realistic representation of distributions of variables that cannot assume negative values (such as sales) than is tiie normal distribution. A normal distribution assigns probability to such events, vjlii Le the lognoriikil distribution does not. Fur tliermore , even though

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130 the loRnormal distribution is skewed, by taking the spread jjarameter small enough, it is possible to construct a lognoi^mal d istr ibut icna closely resembling any normal distribution ( except those with high probabilities of negative values ) . Milliard and Leitch (1975) pointed out the problem of assuming price and quantity to be independent. However, if we assume that sales quantity and contribution margin are joint lognormally distributed then we can allow for statistical dependence among the two variables as we will show later. When it is assumed that sales quantity and contribution margin are both lognormally distribution, there is a closed form expression for the probability distribution of gross profits since the product of tvi/o lognormal random variables is also lognormally distributed. The nonstationary Bayesian CVP analysis is easily extended to the case of a lognormal distribution of Q or to a case where sales quantity and contribution margin are both lognormally distributed. The extension is easy because if x is lognormal then ln~x is normal. Suppose that the distribution of sales is lognormal, i.e., (5.2.16) fLig(Q|p,a-) = [Qav''2^1 exp[-(ln Q P)/2o2], with unknown parameter p and known o^ . Note that if we consider In to be the random variable instead of Q the lognormal distribution is easily transfromed into a normal distribution and vice versa, i.e., (5.2.17) iLN^'^lP'"' ) = ^"^ f^dn-Qlp.o^).

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131 Thus the predictive Cyi' model for norm.il processes presented before can be extended t a'^/n') = f'Cil I 4, .1). For a sample of 12 months with mean 6.2 the posterior distribution will be f"(p | m" , a2/n")=f"(p | 5.2, .0455) The predictive distribution of In Q at period t is (5.2.18) f^Cln^Q Jm", o2[l + 1/n"]) = f,,(ln Q I 5.2, 1.0455), N t ' t t N 1 1 or the predictive distribution of Q at period t is (5.2.19) f^^(()Jm", a2[l+ l/n^'l) = ^i^^iQ^] 5.2, 1.0455). By the properties of lognormal random variates it follows that Q has predictive mean E(Q ) = exp [5.2 + 1.0455/2] = 305/74 and predictive variance Var(Qj^) = [exp (10.4)] w(w-l) where w = exp [1.0455], that is Var(Qj.) = 172,002.72. To obtain probability statements regarding Q it is necessary to translate the iirobability statement regarding In Q using the antilogarl thmic transformation. For instance, as before let the contribution margin be 8 and let fixed costs be 1,000. The probability of making mori' than $3,000 in profits is equal to the probability PAGE 145 132 of selling more than 500 units, [Q^. > (n F)/(P-V)] or In Q >^ 6.2146. Since the distribution of In Q is as in (5,2.18) the probability of profits in excess of$3,000 can be obtained from the standard normal distribution theory. The normal model with unknown mean and variance can be extended to include the case in which the decision maker knows neither the population variance (a^) nor the population mean (p^) The predictive distribution of Q is logStudent when both parameters of the lognormal distribution are unknown. Assuming the prior is of the natural conjugate form, a simple operation transforms the logStudent distribution into a Student distribution; i.e., (5.2.20) fjc.(Q|f(,a^) = Q~^ f^dn^Ql ,1 ,5^) . Therefore by working with In Q instead of Q , the analysis of the normal process can be applied to obtain a Student predictive distribution for In Q unconditioned by the unknown parameters p and g^ Â• To obtain probability statements for Q and it one needs to obtain probability statements for In Q . For instance, suppose that the monthly sales are distributed lognormally with unknown mean and variance and that the predictive distribution of In Q is Student with mean equal to 485 and the variance equal to 5590.32, i.e., Q^. r^ LS ( 485. 5,590.32). Under the assumptions of the previous example, the probability of making more tlian \$3,000 is equivalent to the probability of selling more than 500 units. This probability can be obtained from the standard

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133 Stiidi^nt disLribiiL i on . An impDitant fcaLiire of the model Lli.iL we have developc-d is itri unequal weighting o\ past observations, a characteristic that clearly demoiistrates the problem faced by users that apply stationary inferences when the variables really are nonstationary . It ^^/as pointed out before that the posterior value of the prior parameter m' during any given period t is m'^! = (m'n' + m n)/(n' + n) . Under stationari ty , successively applying this equation gives I'l' , as a function of m' the initial prior mean, of n, the sample size, and of the past sample means. All past observations are weighted equally and m' , can be expressed in the form (5.2.21) m' = (n|m-| + n T. m.)/(nj + tn) i=l ^ or (5.2.22) m;^^ = (n|m' + E Q.)/(n' + tn) , i = l n where Q. = E Q, . ' k=l '^^ Under sta tionarity , n^^-j = n'^ln^/ (n'^'+ n ,) , and n' , < n". If we assume nonstationarl ty with no drift, i.e., u=0, and define q =n'/(n'+n), then tlie posterior value of tlie prior mean parameter in period t+1 is (5.2.23) m^^j = q^m^ + (l-q^)m^. Successi vi'l V applying (5.2.23) gives m'.i as a function of ml, the initial mean, ;ind m. and (| for i = 1 . 2 , . . . , t . 1 1 was shown in 1 i

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134 Appendix I ;md in Cliaptt-r Four that the weight assigned to any observation, say , in determining a prior distribution for P^ , , is a strictly decreasing function of i. That is, the importance of any observed value, say Q ., for making inferences about a future value of the mean, say p , decreases as i increases. For the special case in which n' = n we showed that the prior mean at the beginning of any period under nonstationarity can be expressed as the sum of the initial mean, m' discounted by a factor q, and an exponentially t t-1 i weighted sum of the observed sample means ; i . e ., m' = q m'+ (1-q) S q m .. t+i 1 |=Q t-i To illustrate, suppose that the monthly sales Q , Q , ... of a firm are independent and Identically distributed random variables with common density function f (Q | p, 0^=100) . Assume that the random shock distribution is f (e|0, 50). Suppose also that, at the beginning of period 1, the manager has assessed the prior distribution function to be f'Cp |500, 57.28), To obtain a simpler expression for comparisons with the stationary case, v;e are assuming that at the beginning of the first periotJ the model is already in steady state form in the sense that n! = n = 1.74596 and q =q = ... = .127016. If the manager has available a sample of say, 12 monthly sales with sample mean m = 480 then the mean of the posterior distr ibutitni of p is 482.5403. Since we are assuming that there is nonstationarity with no drift the mean of t lie [jrior distribution of p under stationarity and nonstationarity is 482.5403. If the manager has available, during

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135 period 2, a new sample of 12 monthly sales with a sample mean m^=505 then he may compute a new posterior distribution of p^ whicli reflects this new information that he has available. Under stationarity the mean of the posterior distribution of p^ Vi/ill be (5.2.24) mi; = [1.74596(500) + 12(480 + 505) 1 /[ 1 . 74596 + 2(12)] = 493.0155 . Under nonstationarity with n' = n = 1.74596 the mean of the posterior 1 L distribution of p.^ will be (5.2.25) m" = (.127016)2(500) + (1. 12701 6) [ 505 + (. 127016) (480) = 502.1473 . During period 3 if a sample of 12 observations is available that yields a sample mean m = 520 then the mean of the posterior distribution of Ot will be m" = 501.5895 under stationarity. Under nonstationarity and steady state condition the mean of the posterior distribution of li will be (5.2.26) m'^ = (.127016)3(500) + (1-. 127016) [520 + (. 127016) (500) + ( . 127016) ^ (480) ] m" = 517.7324 . As we move into the future tlie initial prior mean has loss weight in the determination of the prior mean m'. From the exponentially v;eighted

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136 sum of sample meaaa we note that recent sample means are weighted more heavily that not so recent ones. The impact of a particular sample mean on future values of the prior distrihution of u decreases as t increases. 5.2.3 Extensions to the Nonstationary Ba y esian CVP Mo del It is possible to significantly extend the model presented in the previous section by assuming that sales, Q, and contribution margin, (P-V) , are normally or lognormally distributed. For Instance suppose that Q and (P-V) are both normally distributed with unknown means fj and M(-p_y^Â• The predictive distribution of Q and the predictive distribution of (P-V) are normally distributed. It is well known [see Ferrara, Hayya and Nachman (1972)] that the distribution of the product of two normally distributed random variables is not normally distributed. However, if we denote Q* = e^ and (P-V)* = e to be the new random variables then the distributions of Q" and (P-V)* are lognormally distributed. If a conjugate prior is assigned to y, then the predictive distribution of a lognormally distributed variable when y is unknown, is also lognormal; hence both.Q* and (P-V)* have lognormal predictive distributions. However, Patel, Kapadia and Owen (1976) point out that if x-j^ and X2 are independent random variables with probability density functions f(x| 8^,62) and f (x2 |a-j^ ,02) , respectively, then the random variable Y =XjX2 also has a lognormal distribution with probability dL-nsiry function f(Y|0^+ a^, 0.-,+ a2) . Suppose then that Q and (P-V) are both lojinornia 1 Ly distributed with unknown parameters V' and

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137 p. . respectively. Both distributions have lognormal predictive distributions and hence Q[P-V] is lognormally distributed. To illustrate, suppose that at any given period t the predictive distribution of sales (Q ) is given by f [Q Im'' , o'^Cl + 1/n" ] and that tVie predictive dlstrl^ ^ hN^t ' Qt Qt bution of the contribution margin (P-V) is given by ^LNf^^"^^''"'(P-V)t' Â°(P-V)^^ ^ ^^"(P-V)t^^'^''^"' ^^'^ predictive distribution of Q[P-V] is given by (5.2.27) Once we find the predictive distribution as defined in (5.2.21) ue can find the distribution of profits as was explained before. We cannot extend our analysis to the cases where Q and (P-V) are both normally or lognormally distributed with unknown means y and p, , and unknown variances a?^ and o^ .. For the case in which both Q and (P-V) are normally distributed it was shown in Chapter Four tliat the predictive distributions are Student. The distribution of the product of two Student distributiona does not have a tractable closed form except when the parameters of the two distributions are the same. In this case the distribution of the product is an F distribution. If conjugate priors are assigned to the unknown parameters of the distribution of Q and (P-V) then for the case in which Q and (P-V) are lognormally distributed, ihe predictive distributions are logStudent. We cannot extend our analysis to this case either because the distribution of

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138 the product of two logStudent distributions does not have a tractable closed form in the general case, We can address the previous problem from a different view point. Suppose that Q and (P-V) have a joint lognormal distribution with parameters p and Z where (5,2,28) (P-V. and (5.2.29) 9 12 21 Given that \s is unknown and I is known, the decision maker can assess a joint prior distribution on the vector of unknown parameters. A joint predictive distribution for Q and (P-V) can be obtained from the posterior distribution of y. This approach works if Z is known; otherwise there is not a tractable closed form in the general case. The nonstationary Bayesian CVP model presented in the previous section can be extended to the multiproduct case. In any given period t, the random variables of interest are vectors Q of quantities sold for products 1, 2, ... ,P; i.e., Q^= ^Qjil' ^t2' "' ' ^tP^ * ^"PPÂ°^^ ^^'^^^ Q^ is multivariate normally distributed with mean vector u and E covariance. A Bayesian analysis involves the assessment of a prior distribution for p if only the vector of means is unknown or a joint prior on (p ,Z) if both parameters are unknown. After a vector Q is observed, the posterior distribution of the unknown parameters is

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139 available. Next, assume that values of the mean vector for successive time periods are relateil as li . , = u + e , where e is a multi"t+l ,t -, t .t normal "random shock" term independent of p with known mean vector u and covariance matrix U, For the case in which p is the unknown vector of parameters, Winkler and Barry (197J) discuss the methodology to obtain the posterior distribution of jTi and the predictive distribution of the vector of quantities sold, Q , They pointed out that the updating procedure for the model is relatively straightforward but that difficulties are encountered in attempting to investigate limiting properties of the model. Simplifying assumptions which produce limiting results are: 1. the prior information at the beginning of period one can be thought of as equivalent to the information obtained from a sample of size n' from the process and therefore the covariance matrix of the initial distribution, say S' can be thought as a constant multiple of E; i.e., S' = (n')~ T.; 2. the random shocks that change the mean vector from period to period are such that they do not change the underlying relationship among the elements of the mean vector and therefore the covariance matrix il can be thought as a constant multiple of I; say fi= w E. If we make the same simplifying assumptions as in VJinkler and Barry (.197 J) and in addition assume that from period to period the unknown covariance matrix E does not change then we can extend tlie metli-

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140 odology from the univarinte to the multivariate case for the case in which p and Z are both unknown. Under these assumptions, during the time period t we can revise the joint distribution on (fj ,z) and at the end of time period t (the beginning of time period t+1) determine the new mean vector j] , which reflects the effects of the random shock. From the prior distribution of (p ,-, ,Z) the decision maker can determine the predictive distribution of quantities sold and the predictive distribution of profits. 5 . 3 Non statlonarity in Statistical Life Analys is 5.3,1 Existing Ana lysis Reliability theory is the discipline that deal, among other things, with procedures to ensure the maximum effectiveness of manufactured articles. In general, life length is random, and so we are led to a study of Life distributions. For instance. Farewell and Prentice (1977) emphasize the applicability of lognormal models to recent data sets from the industrial and medical literature. Reliability theory emphasizes the prediction, estimation and optimization of the probability of survival, the mean life, or more generally, the life distribution of components or systems, In the traditional approach to life testing inference points or interval estimators for functions of the life distributions were obtained by substituting for the unknown parameters the point estimators obtained for them. Most uses of Bayesian methods can be characterized

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141 as point or interval estimation of parameters of life distributions or of reliability functions. All of the papers discussed in Chapter Two that have considered life testing problems have assumed a stationary situation. However, no matter how hard the company works to maintain constant condition during a production process, fluctuations in the production factors can lead to a significant variation in the properties of the finished products. Variation in inputs, in some cases, tend to be purely random and could gradually change the characteristics of the life distributions of the products. Moreover, the wearout of the machines used in the manufacture of the products could cause changes in the quality of the products and hence in the parameters of the life distributions. Again we want to stress that we are referring to gradual changes, the effects of which are not perfectly predictable in advance for a particular period, i.e., the characteristics of the process vary across time but are relatively constant vjithin a given period. In our opinion the model developed in Chapters Three and Four provides a convenient framework to study the effects of nonstationarity on the inferences drawn from life testing statistical models. 5.3.2 A Life Testi ng Model Under No nstationar ity A natural framework for studying the problem of changing parameters in terms of forecasting the life of a manufactured product is provided by the Bayesian approacli to statistical Inference. Having a product, let us consider the random interval beginning with the

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142 moment the product starts to work and ending at the moment of its failure. This positive random variable is called life time of the product or time to failure. Suppose that the model for the life of a product is the lognormal distribution with parameters y and o'^ ; i.e., the life of products coming from a given process, L^ , h^ , ... , are independent and identically tlistributed random variables with common density function f (lIp.o'^). Suppose also that a posterior distribution over the unknown parameters is available and that the distribution of the random variable, life, undergoes a gradual parameter shift between successive periods of time of the form \i = p^ + e,-,-i as defined in (3.3.8). From a formal Bayesian analysis, during a given period t, two distributions are available, namely the posterior distribution and the predictive distribution of a future observation which comes from the same data generating process. If pj. is the only unknown parameter and the prior distribution is natural conjugate to the process then tlie posterior distribution is f"(M|.|m",a'^/np and the predictive distribution is ^LN^^tl'^t'^Sld + n^^^)/n^^^]), as defined in (3.3.6) and (3.3.7). If pj. and 6 are both unknowi and the prior distribution is natural conjugate to the lognormal distribution tlien the posterior distribution is fM_--.(f't ''^^ l'"t ' "t' ^" ^^'^ "^"-^ ^^^ ^^^ predictive distribution is logStudeiit witli infinite mean and variance, as defined in (3.3.17)(3.3.20).

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143 Under, both uncertainCy situations the posterior distribution (or the prior if no sample evidence was included) then reflects whatever is known concerning the parameters of interest and it also fully reflects the remaining uncertainty the manager has concerning the parameters. A large part of the statistical problem in reliability involves the estimation (point or interval) of parameters in failure distributions. Each of tlie non-Bayesian methods of obtaining point estimates given in Chapter Two has certain statistical properties that make it desirable from a theoretical viewpoint. From the Bayesian standpoint the posterior distribution should be used to derive the point or interval estimators of the unkno\vm parameters, except under nonstationarity in which case the new prior should be used. With respect to inferences, the manager considers the entire posterior distribution (or any probability determined from this distribution) as an inferential statement, and he may not be interested in a single point estimate. For instance, some potential estimators of p based on the normal posterior distribution, for the case when only jj is unknown, are the posterior mean, the posterior median, the posterior mode, and so on. Since the normal is unimodal and symmetric, the posterior mean, m" , is equal to the posterior median and to the posterior mode. On the other liand if an interval of values for fj rather than a t single value is desired tlien from the normal posterior distribution, the probability of any interval of values of \i can be determined. It was sliown in Chapter Three that the presence of nonstationarity produces

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144 greater uncertainty (variance), at the start of period t+1, with respect to the unknown parameter than would be present under stationarity because in the stationary case n' , = n'' . Thus we would expect to have wider intervals for a given y content; and after several periods the intervals will also be shifted in location since they will differ in means. For the case in which both parameters (jj and 6^) of the lognormal distribution are unknown, some potential point or interval estimators are based on the marginal distributions obtained form the joint posterior distribution function. In any given period t if the joint posterior distribution of the unknown parameters of the lognormal life density function is normal-gamma, as defined in Section 3.3.2, then the marginal distribution of o is inverted-gamma-2^ as defined by (5.3.1) 2 exp[-d'V72a2] [d"v"/2o2 ] ^^^t/^) + 1/2 f. o(okv", d") = ^-^ ^-^ ' l-Y-2 ' t t . 1/9 ^ r(d'72) [d"v'72] ' with mean (5.3,2) E(o|v'^, d'^) = /VVd'^72 fd'72 3/2] !/ [r'(d'72) ] The marginal distribution of l/d^ is garama-2,

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145 and variance (5.3.3) V(0|v'j:, dp = [v^M'^/(d'^' 2)] [E(o)]2. The cimiulative density function of the inverted-gaTnina-2 is related to the cumulative density function of the gamiiia-2 variable by (5.3.4) G._^_2(o|/^, dp = F^_2^1/e^iv;:. d'^) , 00 a where G(a) = / f(x) dx and F(a) = / f(x) dx, [see Raiffa and Schlaifer a (1961) J. The marginal distribution of p is Student as defined by f-,(0^|ni'J> ii'J/v" ,d'') . Point or interval estimators may be obtained from (5.3.1) or from the Student marginal distribution of fl . Sometimes the people working with life testing models are interested in the distribution of the median and of the mean of the lognorm;illy distributed variables. The median and the mean of lognormally distributed random variables are given by C= exp (|j) and 5= exp(y+ a^ 1 2) . For given period t, the conditional posterior probability density function given o is norma] with mean m" and variance a /n", then C, given T, has a lognormai posterior probability density function. The marginal posterior probability density function for jj is Student; thus C has a posterior density function which is logStudent [Zellner (1971)]. Similarly, givi?n o, the conditional posterior probability density function lor 5 is lognormai. Again these distributions incorporate all the available prior and sample information and can be employed to oljtaiii point estimates, to make probability statements

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146 about parameter's values, to perform Bayesian tests of hypotlieses and to derive predictive probability distrlbut ion.s for future observations coming from the lognormal life testing model. As discussed in Chapter Four, a prediction interval is different from a confidence interval for an unknown population parameter (such as the population mean) or from a tolerance interval to contain a specified proportion of the population. It is sometimes of interest to obtain a value, arrived at by life testing, that with high probability will be less than the life length of a particular component that is to be used in a one trial system. In many practical problems in industry, it is desired to use the results of a previous sample to predict the results of a future sample. For example, data on warranty values on engines over the past three years might be used for planning purposes to obtain limits that will contain the warranty in the coming year with a high probability. Such problems can be handled by Bayesian prediction intervals. Prediction intervals are also of special interest to engineers who are concerned with setting limits on the performance of a small number of units of a product. Such limits would be required, for example, in setting specifications to contain with a high probability a critical performance characteristic for all units in an order of three heavy transformers when the only available information is the data on five previous transformers of the same type. By using the limits of a prediction interval as specification limits, one can state that wi tli a specified probability all three transformers will meet specifications [Hahn and Nelson (1973)].

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147 Prediction intervals are also required by the typical customer who purchases one or a small number of units of a given product and who must set limits on the performance values of the particular units he will purchase. The prior mean, at the beginning of any period, (m' ) , under nonstationarlty can be expressed as the sum of the initial mean, m' discounted by a factor that decreases with time, and an exponentially weighted sum of the observed sample observations. This relationship provides probably the most interesting aspect of the nonstationarlty Bayesian model, particularly for tlie life testing problem. Since most of the point and intervals estimates discussed in previous paragraphs are functions of n' , then they are also unequally weighted functions of past data. This gives a Bayesian interpretation and justification for the old production management idea of exponential smoothing . A strong argument is made that since the most recent observations contain the most information about what will happen in the future they should be given relatively more weight than the older observations. A limitation in exponential smoothing techniques is that there is no good The exponentially weighted moving average forecast arises from the following moilel of expectations adapting to changing conditions. Let y^. represent that part of a time series which cannot be explained by trend, seasonal, or any other systematic factors; and let y represent the forecast, or expectation, of y. on the basis of iiiformation available through the (t-l)st period. It is assumed that the forecast is changed from one period to the next liy an amount proportional to the last observed error. That is. y^^ = y , + (Â•i(yj._^y,-_^) , 0< B <1. The solution of the above difference equation gives the formula for the exponentially weighted forecast : y^ = \-> T. (l-ii)^"-^ y^_. . 1=1

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148 rule for determining tlie appropiate value of the weights to be assigned to each observation. The nonstationarity Bayesian model provides a rule to determine the set of weights to be assigned to the observations, 5 . 4 Conclusion In this chapter, Bayesian models for Cost-Volume-Profit Analysis and for life testing models under nonstationarity have been presented. This is reflected by the assignment of a prior distribution to the unknown parameters, which recognizes all uncertainty the decision maker has concerning the parameters. The input to the forecasting model is not only the past history of sales of the item, in the case of CVP analysis, but direct Information concerning the market, the Industry, the economy, sales of competing and complementary products, price changes, advertising campaigns, and so on are used. A similar amount of information is incorporated from life testing models. The model also emphasizes that such a model ideally should include the changing character of the parameters of economic and life distributions by allowing for changes in the parametric description of the process through time. For the case of nornial and lognormal data generating processes, under a particular form of stochastic parameter variation it is shown that the presence of nonstationarity produces greater uncertainty to the manager, whi( h is reflected in these particular cases by an increase in a particular iiu^asuro of imcertainty, variance. Bayesian methods are used to derive predictive distributions for CVP analysis and life testing

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U9 models allow the decision maker to provide probability statements about future values of sales and future life length of items. Estimates obtained from the posterior and predictive distributions are unequally weighted functions of past data.

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CHAPTER SIX CONCLUSIONS, LIMITATIONS, AND FURTHER STUDY 6 , 1 Summary Great effort has been expended by engineers, econometricians and statisticians over the last two decades on the problem of model identification. This problem is concerned with construction of a model whose output is close in some sense to the observed data from the real system. The equations which describe the model are often specified to within a number of parameters which must be estimated. The unknown parameters are usually assumed a priori to be constant. In this case the problem of model identification is reduced to one of constant parameter estimation. The problem of time varying parameters has received more attention during recent years because of an increased body of evidence that the usual assumption of stable parameters often lacks realism. The stochastic parameter variation problem arises when parameter variation includes a component which is a realization of some random process in addition to whatever component is related to observable variables, Ideally, a model would be so well specified that no stochastic parameter variation would be present, but the world is less than ideal, In this dissertation we extend and generalize an earlier model developed by Winkler and Barry (1973) by 1. explicitly accounting for uncertainty with respect to both parameters of the Uayesian normal model, 150

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151 and 2. model iag nunstationarity in mean and vari;mce for the lognormal case, since the mean and variance of the lognormal distribution are both functions of both \^ and o^ Â• Some of the objectives of this kind of research are to gain more precise information about the structure of economic relationships and/or to obtain estimated relationships that are suitable for forecasting, in particular in the areas of CVP analysis and life testing models. Tlie model developeti in the previous chapters seems particularly appropiate to both of these objectives, because it [provides a framework for drawing inferences about the structure of the relatlonshi]^ at every point in time. Comparing the nonstationary model with the stationary one it is shovm that: 1. more uncertainty is present under nonstationarity than under stationarlty; 2. past observations provide relatively less information about the current values of p under nonstationarity than under stationarlty because the particular form of stochastic parameter variation used implies a treatment of data involving the use of all observations in a differential weighting scheme; and , 3. under nonstationarity the limiting values of some of the parameters of the [>osterior and predictive distributions cannot be determined clearly.

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152 Tlie model developed in this dissertation is simple and some of the results arc obtained under very restrictive assumptions. F'robahly the most important advantage of the new v;ork is the increased versatility it lends to the nonstationary Bayesian model derived by Winkler and Barry (1973), i.e., the enlarged range of real and important problems involving univariate or multivariate nonstationary normal and lognormal processes v.'ith which it can cope. Another advantage is tliat it keeps the simplicity of the updating methods for the efficient handling of the estimation of unknovm parameters and the prediction of the outcome of a future sample. 6 . 2 Limitations The results obtained from the Bayesian modeling of nonstationarity rely on some general and simplifying assumptions that we have pointed out throughout the dissertation. Some of these assumptions limit the results obtained from the model. These are assumjjtions that are part of the more general Bayesian statistical inference model and others are related directly to the nonstationary condition. The decisions we make, the conclusions we reach and the explanations we offer are usually based on beliefs concerning the probability of uncertain events su(.:h as the result of an experiment, the outcome of a sport event or the future value of an investment. In general, we do not have objectively given models according to v;hich the probability of such events could he computed. As a consequence, the assessment of uncertainty is often based on the intuitive judgments of lumian beings. One .important assumption ol tlie model that v/e developed is that t'ne manager can express

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153 his judgments about the unknown parameters in terms of a natural conjugate prior distribution for the process, Tlie manager has to decide which parameters are unknown and then he must express his information about these random variables in probabilistic terms and according to the natural conjugate family of prior distributions. The prior probabilities should reflect the decision maker's prior information about the uncertain quantity in question, i.e., sample results if available and if there is little or no sample information, then they should be based on any other relevant information available. Several techniques are available for the quantification of judgment; some of these were referenced in Chapters Two and Five. For many problems, a joint distribution for the unknown parameters is needed, If the uncertain parameters are dependent, the assessment process becomes difficult, especially if we are dealing with continuous random variables. The applicability of conjugate prior distributions depends in part on the appliv-.abi li ty of a particular statistical model because the conjugate family of distributions, as shov\7n in Chapter Three, depends on assumptions concerning a statistical model. Although the model is originally developed for normal data generating processes, several references are given for the applicability of lognormal models to economic and life testing problems. There are cases in which even if a certain model is applicable to the dtita generating process and if the corresponding conjugate family is known, it may be that no member of the family adequately represents the assessoT''s prior juilgments.

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154 For some of the results, we assumed that for each period a sample of equal size, n, is available. This is a crucial assumption for the limiting results discussed in Chapter Four. If a sample of equal size is not available, each time a sample is taken, then the limiting value of n' cannot be obtained without some further restrictions on the nature of the sampling procedure actually used. The imposition of the transition relation p ,, = p + ^^ , is critical to the determination of the prior distribution of the time varying coefficient. We assumed that the distributions of p and e were normal, and therefore we were able to find the convolution. It is shown in Appendix II that other assumptions like gamma or exponential random shocks, non-additive nonstationary models, i.e., P , = P e^ ^ Â» and exponential data generating processes can lead to distributions that are not tractable and consequently not useful for the Bayesian modeling of time varying parameters. It is also assumed in this model that no seasonal or trend effects are present. Insofar as the model is used for shortterm forecasting, this assumption does not seem unrealistic. Further research including these additional sources of variation could lead to a more versatile model, although problems like those discussed in Appendix II are likely to reduce the possibilities of obtaining a model in closed form. [See Harrison and Stevens (1971) for some results with such a model.] The assumption that the variance of the normal process is known seems particularly unrealistic when we are assuming tliat the mean is unknown. Thus, we assumed that both parameters are unknown. However, a restrictive

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155 assumption has to be iinposeil in order to permit tlie determination of the new prior distribution after a random shock has occurred, i.e., that the ratio (n ) between the unkno\
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156 We have assumed that a change in the process mean takes place during each period and that the magnitude of that change, e, has a distribution N(0,o'^). Although this has been a convenient assumption, it e perhaps lacks realism. A more realistic assumption would seem to be that an assignable cause (and hence a change in the process mean) occurs according to a Poisson process. Carter (1972) approached this problem assuming that o'^ , the population variance, was known. The methodology described in this dissertation for the case in which both parameters are unknown could be used incorporating this new assumption to the problem. The probler.i of nonstationarity could be approached from a different angle. Suppose tliat the time varying parameters y and p are independent and identically distributed, conditional upon some second order parameter (s) , instead of being related in a stochastic manner. In a problem like this the decision maker is making inferences about the distribution of p , which sometimes is called the distribution of nonstationarity. For instance, if y is the mean for period t of a normal data generating process for sales of a given company, then the distribution of nonstationarity might represent the different values of M over time. In general, the distribution of nonstationarity will have a parameter (or a vector of parameters) often denoted by (f" , so that the distribution of nonstationarity can be represented by f(Pj,|'||) for all t. A Bayesian approach to this problem requires the specification of a probability distribution for f((t') in order to express the decision maker's uncertainty about *)' . This problem can be studied

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157 under various uncertainty and distrilnitional assumptions concerning tlie distribution of nonstat ionari ty and concerning the distribution of second order parameters, i.e., f (<()). This problem is related to the problem studied by a class of theorists known as Empirical Bayesian [see Maritz (1970)]. Another application of the model developed in this dissertation relates to calibration of Instruments. Suppose that a product is being weighed. During period t a sample is taken to estimate the average weight of the products, p . As the average appears to be high or low, a dial can be set to increase or reduce the average weight of the products by an amount c . If we assume that the dial is poorly calibrated, i.e., e becomes a random variable, then when we change the dial we do not get V "*" '\but rather M + e , where E(e ) = e . Since the setting varies, Â£ will vary and hence the expected mean weight of the products for the next period of time, ECfi ,) will vary. The expected value of e , e , might be subject to control, so that a decision problem arises. Each period of time a setting must be selected tliat minimizes the variance of the average weight or that minimizes the predictive variance of the weight for a f utut e product that is sampled, or that satisfies a probabilistic constraint on the next weights of items produced by the process. Perhaps the most important area for further work has to do with identification of the nonstat ionarity , We have stressed throughout the dissertation that it is important for the decision maker to recognize Che presence of nonstationar i ty if it exists. However, most of the time it is very difficult to get information about the general form of nonstationarity . Analyzing data for evidence of changes in [larameter

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158 conditions is a problem central to the development of an inferential system that the decision maker can use. The decision maker has available the sequence of sample means m^ , m2 , ... , m^. , ... . More research is needed to find out how those sample means could be helpful in determining what form of nonstationarity is present and what its variability is. In the previous section we pointed out that, when the parameters ]s and o^ are unknovm, our model depended on the assumption that the ratio (n .) between the unknown population variance and the random shock variance is known. In most cases the decision maker does not know this value and needs to estimate it. Additional research is required to find out how to use the sample means and the sample variance to estimate n . s In conclusion, since assumptions of stationarity are often quite unrealistic, the introduction of possible nonstationarity greatly increases the realism and tlie applicability of statistical inference ' methods, in particular of Bayesian procedures. More work, of both an empirical and analytical nature, appears to be promising.

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APPENDIX I APPENDIX TO CHAPTER THREE

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APPENDIX I APPENDIX TO CHAPTER THREE Bayesian Analysis of Normal and Lognormal Processes The general Bayesian theory presented in subsection 3.2.1 provides the foundation for the analysis of normal and lognormal processes to be considered in this appendix. Most of this \\;ork appear in Raiffa and Schlaifer (1961) and in De Groot (1970). It sets the stage for our analysis of normal and lognormal processes under nonstationarity in section 3,3. Two uncertainty conditions are to be studied in detail ; in one the shift parameter, y, is unknown and the spread parameter, a^, is assumed to be known, and in the other case both parameters are assumed to be unknown. Prior, posterior and predictive distributions will be determined for both cases. In every case sufficient statistics will be found for the unknown parameters. I . 1 Normal and Lognormal Processes with Known Spread Parameters The purpose of an experiment is to obtain information about p or o , depending upon which (if either) is known beforehand. Consider experiments consisting of n independent and identically distributed observations x , xÂ„, ... , X obtained from a normal process; that is a process generating random variables x , x , ... , x with identical densities (AT.l) fj^(x|M,a2) = {/l^ a)~^ exp [-(x-p) 2/2a2] , -Â«>< x <" , Â— OD< y 0. 160

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161 The likelihood thaL an Tiuiependent Normal process will generate n successive values x,, x^, ... , x is the product i)f tlieir individual likelihoods as given tiy (Al.l) if the stopping process is noninformative. (See La Valle (1970) for a general discussion of stopping rules.) In other words it is the product of their individual likelihoods if the kernel of the likelihood function for tlie parameter depends only on the data generating process and not on the stopping process. We will assume that the stopping process is noninf ormative. Therefore the likelihood could be written as. n (AI.2) l(^:|fi,a^) = ][ {[/Trr'o] ^ exp [-(x . -p) 2 /2o2 ] ) i = l or (AI.3) =[/2^ or" exp{-[ E (x.-p)2]/2a2} . i=l If we assume that jj is unknown, then we can compute the statistic m defined as n (A1.4) m = ( T. x^)/n. i=l The likelihood can be written as, (AI.5) l(x|p) = (/2^ a)~" (exp{-[ Z (x .-m)2 /2o2 ] } ) exp [-n(m-p)2/2o2 J i=l (A1.6) a exp[-n(m-p)-/262 ] .

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162 Thus all the information in the sample is conveyed by the statistics m, sample mean, and n, sample size. Since the data enter Bayes' formula only through the likelihood, it follows that all other aspects of the data, with the exception of m, are irrelevant in determining the posterior distribution of \i and hence in making inferences about yRaiffa and Schlaifer (1961) show that when the variance, o^, of an independent normal process is known but the mean is treated as a random variable, the most convenient distribution of y, the natu-ral conjugate prior, is the normal distribution defined by (AT. 7) tj^(plm,a'2) = {exp [-(p-m) 2/2o' 2] }/a' 2/2T, Â— < y <-, a'2 > 0. In the particular case of an unknown mean, the likelihood of p is a normal curve completely known a priori except for location, which is determined by m. That is, the likelihood is data translated in the original metric p and therefore a noninf ormative prior is locally uniform in p itself, To simplify our results, let o'^ =-o2/n'; that is we define the parameter n' by (AI.8) n' = o2/o'2 and say that the information, (m,o'2), contained in the prior distribution of fi is equivalent to n' observations on the process. In otheiwords let the prior distribution be

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163 (AI.9) fjljC M |m,a2,n) = {expl-n( u-m)2/2o2 ]]/a/2u/n . If a normal distribution with parameters n' and m' is assigned to u and if a sample then yields a sufficient statistic (m,n) then the posterior distribution of y will be a normal distribution with parameters, (ALIO) m" = (n'm' + nra)/(n'+ n) and (AI.ll) n" = n' + n . It can be seen in (ALIO) that m" is the weiglited average of the prior and sample means. Therefore, we may conveniently regard the mean of the posterior distribution as a weighted average of an estimate of p formed from the sample and an estimate of u formed from the prior distribution. The weights of m and m' in this weighted average are proportional to n' and n. If n'>n, the prior mean is given more weight, and the posterior mean m" is closer to m' than to m. If n'
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164 increases by a constant amount with each observation that is taken, regardless of the observed values. Therefore as the number of observations increases, the distribution of p becomes more concentrated around its mean. Moreover, the concentration must increase in a fixed, predetermined way, while the values of the expectation of the mean will depend on the observed values. If the n random variables x^ , ..., x represent a random 1 n '^ sample of size n from a normally distributed population with mean M o and variance o , then the sample mean m is normally distributed with conditional mean E(m|y,a'^) = p and conditional variance V(m| p ,o^ )=a'^/n. Since the variance of tlie prior distribution is equal to o^/n' and the variance of the sample mean is equal to o^/n, we notice that, in the posterior distribution, the prior information receives more weight than the sample information if the prior variance is less than the variance of m (i.e.,n'
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165 predictive distribution function is a normal distribution with mean m" and variance (AI.13) o-Xn" + l)/n" = o"' + (o^/n") . Thus the predictive variance reflects both the process variance ^ and uncertainty about \i measured by o^/n". We are also interested in studying experiments consisting of n independent and identically distributed observations x , xÂ„ x 12 n obtained from a lognormal process, that is a process generating random variables x , x , ..., x vs^ith identical densities, (A1.14) f (x|p,o^) = {exp[-(ln X -& )^/2a2] )/xo /2tt , x > 0, LN _ oo<(j o > 0. It was stated in Chapter Two that a random variable x is said to be lognormal if and only if In x is normal. That is, suppose that In X is normal with unknown mean jL and known variance a^ . Denoting by f^,(ln X 1 u, ,0^-) tlie value of the normal density at In x and by N ' Ij 1. f (x|vL ,0"^) the value of the lognormal density at x, it follows that (AI.15) ^LN^^l^'^^L^ = ^N^^" x|wj^,a2)/x . Thus working in terms of the variable In x, the preceding analysis in terms of the normal process can be applied to obtain results that apply to tlie lognormal distribution. When it Js assumed, in a lognormal distribution, that o is

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166 kno\m and that p is unkno^sm it follows that the sufficient statistics are, (AI.16) m = ( ? In X )/n ; i=l 1 and n. The natural conjugate prior for the unknown parameter y is normal v/ith parameters m' and n' as in the normal case. The revision of the prior distrihution is similar to the normal case also. If a lognormal distribution with parameters m' and n' is assigned to y and if a sample then yields a sufficient statistic (m,n) then the posterior distribution of y, will be normal with parameters, (AT. 17) m" = (n'm' + n m) / (n ' + n) , and (AI.18) n" = n' + n . The predictive distribution will be lognormal with parameters m" and o2(n" + l)/n". I . 2 Normal and Lognormal Processes with Both Parameters Unknown We shall now consider the important problem of sampling from a normal distribution for which both mean and variance are unknown. A conjugate family for this problem must be a family of bivariate distributions. Suppose that x^ , xÂ„ , . . . , x is a random sample from i 2 n a normal distribution with an unknown value of the mean, y, and an unknown value of the variance, a^ . The likelihood that an independent normal process will generate such a sample is given in (AI.3), if the

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167 stopping process is noninf ormative . Now, if we define the statistics, n (AI.19) 111 = ( F, x.)/n, i=l ^ and n (AI.20) V = ( I (x.-m)-)/(n-l), ( E if n=l) , i=l ^ the likelihood (AT . 3) could be rewritten as, (AI.21) -n -n l(x|p,o?) = (2it)" (exp [-{(n-l)v/262} _ {n(m-P) ^/2a^ }] } (6)^. All the information in the sample is conveyed by the statistics m,v, and n; i.e., (ni,v,n) is sufficient. The kernel of tlie likelihood is -jn (A1.22) {exp[-{(n-l)v/2d2} {n(m-u)'^ /2d'']]} (o)^. Raiffa and Schlaifer (1961) show that under these assumptions, the natural conjugate family of prior distributions for the two random variables, p and 6", is a normal-gamma-2 distribution defined by (AI.23) Fj^_-^_2(P'^^U.v,n) = f^(p|d2,m,n) f^_2(o2 | v,n) ; that is (AI.24) e 2o' ^ v(n-l) j 2 ^(n-l)v^ fN-Y-7^^'"^'"''^'"^ = I /^^exp[-n(P-m)2/2n2l}{ ^"^ _, ~Â— 2110 2 r ("-^i) -^< p <-., n,v >().

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168 They recommend also that, in order to improve the richness of the prior joint distribution we could define a new parameter, d, which could be called the number of degrees of freedom in the statistic v. It does not have to be equal to n-1 since f^,(p d ,m,n) and f Â„(()"" |v,d) are distinct. The prior joint distribution could then be defined as (AI.25) 1\, (M,a2|m,v,n,d) = f^,(p | o^ ,m,n) f Â„(a^|v,d). N-y N ' y-z ' We want to point out that, if we are concerned with noninformatlve prior, then in order to find this tractable prior distribution a metric log a and not o should be used, In other words the metric (transformation) log o permits us to have a prior distribution of p and a^ that is locally uniform (noninformative) with respect to the likelihood. However, there is not such a restriction when we are working with informative priors. Next we want to present the marginal distributions of 5^ and p since we will make use of them in Section 3,3, where we develop a model for nonstationari ty in normal and lognormal processes. If the joint distribution of the random variables (p,o^) is normal-gamma-2 as defined before. Box and Tiao (1972) show that the marginal distributions of o^ is gamma-2 with parameters v and d, that is (AI.26) d 2 -1 f 2^"^'^'^^^ = {exp[-dv/2d-]} [vd/2a2] [dv/2]/r (d/2) , d2 > 0, v,d > 0. Also they show that the marginal distribution of p is the Student dis-" tribution with parameters (m,v,d,n), that is

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169 (AI.27) d d+1 l'5,j^,(li|m,n/v,d) d^ [d+ln(lj-m)2/v}] ^ /n/v/3 ( i , d/2) , Â—< y <-, d,(n/v)>0 where 3(p,q) Is the complete beta function. If a sample yields a sufficient statistic (m,v,n,d) and a normal-gamma-2 prior with parameters (m' ,v' ,n' ,d ' ) is assigned to p and o^ then the posterior distribution will be normal-gamma-2 with parameters ni" , n" , d", v" given by (Al,28) m" = (n'm' + n m)/(n' + n) , (AI.29) n" = n' + n , (AI.3n) d" = d' + n , and (AI.31) v" = (d'v' + n'm'-+ dv + nm2-n"m"2) / (d ' + n) . To find the predictive distribution of the random variable X, we have to evaluate the expression (AI.32) f(x) = r r f^,(x|p,o2) f-; (vi,o2|m",n",d",v")d,i da -Â°^ Substituting the corresponding functions into the expression and integrating out p and 0-' Kaiffa and Schlalfer (1961) show that the predictive d i str 1 but ion is a Student dist rlbutloa, defined as 2

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170 ^AT.33) d" 2 r M. Â„^9,_.,...n,.^.^-(d" + l)/2 , , (d")^ [d"+[n" (x-m")2/v"(n"+l )]] ^ " / n" , Â— < x <-, ^^ B [(l/2),(d'72)] V'(n"+1) d". n" >0. v"(n"+l) The prior-posterior analysis of the lognormal distribution under the assumption that both parameters are unknown is very similar as we mentioned before to its normal counterpart. The sufficient statistics are n m = ( E In x.)/n , i=l ^ n V = ( Z (In X. m)2)/(n 1) , i=i and n. The natural conjugate prior distribution for both unknown variables is the normal-gamma-2 as defined in (AT. 25), and the marginal distributions are gamma-2 and Student for the parameters 5^ and jj respectively. A posterior analysis will lead us to a norraal-gamma-2 posterior distribution with parameters revised as. in (AT. 28 AI.31). The predictive distribution of In x is Student and hence the predictive distribution of x is logStudent. Ohlson (1977) shows that if the logarithms of the values of a random variable follow a t-model, then the expected value and the variance are infinite. Thus tlie predictive distribution of x, in our case where both parameters are unknov^m, has . Infinite mean and variance. In Chapter Four we will discuss the implications of these properties for our statistical inferential model.

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APPENDIX II APPENDIX TO CHAPTER THREE

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APPENDIX II APPENDIX TO CHAPTER THREE Nonstatlonary Models for the Exponential Distribution In Chapter Two we pointed out that the exponential distribution was frequently used to represent life testing models. All the research in the area of life testing where the distribution has been used has assumed stationary conditions for the parameters of the model. We wanted to model nonstationarity for this distribution using two different noise models, but it proved to be fruitless. Only under very trivial assumptions did the analysis yield tractable results. For the more interesting and realistic assumptions, we will show in this appendix that useful results cannot be developed. In particular these two noise models were considered: one assumes that the value of the parameter of interest, say A, at time period t+1 is equal to the value at time t plus a random term, i.e., (AII.l) A^_^^ = A^ + e^.^^, t = 1, 2, ... ; the other noise model assumes that the value of the parameter A at time period t+1 is equal to the value at time t, tim.es a random term, i.e., (All. 2) \^_^^ = He^,^^, t = 1, 2, ... . Consider experiments consisting of n independent and identically distributed observations x, , X2 , ... , x obtained from an exponential process; that is a process generating random variables x, , x^, ... , x with identical densities. 17:

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174 are equal , i . e. ,3=b; and in the other case we make no restrictions whatsoever in relation to the parameters. Clearly case one is a special case of case two. Mood, Graybill and Boes (1974) state that if T and T are independent continuous random variables and if z = T, + T,^, then the convolution has a density function given by, OO (All. 6) f(z) = / f^ (z-T ) f (T ) dT Since X and e are necessarily positive, the convolution of them will have a density function (ATI. 7) g(z) = /^ f (z-Ala,e) f^ (A|a,b) dA . e ' A ' But in case one, the scale parameters are assumed to be equal to a constant, say to c. Thus equation (All. 7) becomes (All. 8) '^ , . nC-I r / ,N / -. r , / -, ^-1 g(z) = / (z-A)" ^ exp[-(z-A)/c]exp[-A/c.]A" Vr (a)c^r (a) c"* dX . Since z is fixed and A cannot be greater than z we could define a new variable, (All. 9) A = uz < A < z , or (All. 10) u = >/z , . < u < 1 .

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173 (AIL. 3) f (x|a) = A exp(-Ax), x > 0, A > 0. When the stopping process is noninformative the natural conjugate prior distribution of tlie unknown parameter A is the gamma distribution with parameters a and b; i.e., (All. 4) f (A|a,b) = a"""^ exp[-A/B]/r(a)b'^, < A < b, a > 0, b > 0. In any given period t, with the prior on A and with the sufficient statistics from the sample we could find the posterior distribution on A , which will be a gamma with parameters a" and b". At the end of period t, if there are nonstationary parameters, we use the posterior distribution on A and the relation between A^^ and e^,-, to get the prior distribution of tlie unkno;-m parameter at the start of the next period. Assume that a gamma random shock is imposed on the unknown mean, A, of the exponential data generating process; that is (All. 5) f^(ela,3) = e""^ exp[-e/3l /r(a) 3" < e < 3, a > 0, 3 > 0. Furthermore assume that equation (AII.l) describes the nonstationary random shock. Two cases are worthwhile to look at under this scenario-; in the first case vje additionally assume that the scale parameters

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175 Substituting (All. 10) in equation (A1I,8) and simplifying, g(z) becomes, (All. 11) Thus the prior distribution of the mean at the beginning of time period t+1 is gamma again with parameters (a' = a" + a; c) . However, the assumption that the scale parameters are equal makes this result not very useful. It is much more reasonable to think that the distributions of A and of e have not only different parameters a and a but also that they have different scale parameters b and 3. The convolution z of the random variable A, given by equation (All. 4), and the random variable e, given by equation (AIT. 5), when all the parameters are different could be written as (All. 12) g(z) = /^ (z-A)'^"-^ expl-(z-A)/3]A''"^exp(-A/b)/r(a)3'^r(a)b'' dA, or (All. 13) exp (-z/g) Â„ _T _l ^^^* ^ r(a)b''r(a)b'' ^ ^^~^^Â° exp(A[(l/y)-(l/b)])A'' dA. Gradshteyn and Ryshik (1965) show that

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176 (ATT. 14) J X (u-x) L'xp(|,x) dx = 3(u,v) u I" (v;;i+ v;[3u), where BCy^^Yi) is the heta function, and F (v,p+ v,(ju) is a degenerate hypergeometric function wh i ch does not have a_ closed form . Substituting (All. 14) in equation (ATI. 13) yields (All. 15) exp[-A^ ^^/,]A;;^-^ ,F^(a,a^;[(l/3)-(l/b)]A^^^) ^ g(z) = g(>^,+i) = :; '^^ e"b"r(a+a) It is clear from expression (All. 15) that we cannot have a tractable expression to work with in future periods. Furthermore if we assume that at the beginning of period t+1 , the random variable A has a density function of the form given by (All. 15), and if in addition we assume that new information is available that comes from an exponential process, then the posterior distribution cannot be shown to be of the form (All. 15) The previous analysis assumed that the random shock model was of the form (All.l), that is A , = X + e . If we assume now that equation (ATI. 2) describes the nonstationarity condition on the mean of the data generating process, i.e., A .^ = A e ,^, then we could show ' t+1 t t+1 that even in tlie simple case where both scale parameters have a value of one we could not find tractable results. In any given period t.

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177 assume that the posterior distribution of X is given by (All. 4) and that the distribution of e is given by (ATI. 5). Mood, Graybill and Boes (1974) state that for two independent continuous random variables, X and y, the distribution of their product z,i.e,,z = xy, is given by (All. 16) f(z) = /" {f (x,z/x)/|xl} dx . -00 xy ' ' Hence since A is positive, the distribution of the product of the posterior of X and the nonstationary random shock 6 is given by (All. 17) f(2) = /" {A^"^exp(-A) [z/A]'*"-^exp[-z/A]/|A|r(a)r(a)} dA , or a-1 z _ (All. 18) f(z) = ^, .^, . r A^ " -'exp[-A-(z/A)] dA . r^a)r(a) Gradshteyn and Ryshik (1965) state that (All. 19) r x''~^exp[-(e/x)-Yx] dx = 2(B/y)'"'^ K (2/^7) , ^ where K is a Bessel function of imaginary argument. Thus using the relation (All. 19) in (All. 18), f(z) becomes (All. 20) f(z) = z^~^ ItS^"^^''^ K (2/^)/r(a)r(a) . v This shows that even for the simple case where g=b=l, the results are not tractable. Additional problems of interest, like those studied in the previous section, present additional complications. Instead of assuming a gamma random shock we could assume an

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178 exponential random shock to model nonstat lonary means in the data generating process. Consider samples of n independent and identically distributed observations x , x^, ... , x from an exponential process as defined in (All. 3). Assume a gamma prior distribution for the unknown parameter A as defined in (All. 4) and that an exponential random shock is imposed on the unknown mean X, i.e., (All. 21) f (6|it,3) = a exp[-ae], < e < g , a > 0, B > 0. If the equation that describes the nonstationary condition of the mean is (AII.l) then two cases are relevant for analysis: in one we assume that a=l/b and in tlie other we do not make assumptions about the parameters. When we assume that a and 1/b are equal to a constant, say w, then cinivolution z of the random variables A and e has a density function given by (All. 22) f(z) = /^ {w exp[-w(z-A)]A''~''" [exp(-Aw) ] w''/r(a) }dA, or integrating, (All. 23) f(z) = w"""*"^ [exp(-wz)]z''/r(a) a . If we define, (All. 24) w = 1/d, and (All. 25) c = a + 1

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179 and substitute them in (AII23), the density of z becomes (All. 2b) f(z) = exp[-z/d] z''~Vd'' r(c) , vv^hich is easily recognized as a gamma distribution with parameters c and d. If we do not make assumptions about the parameters, the convolution z has a density, (All. 27) f(z) = / {a exp[-a(z-A)] A^"-*" exp(A/b) }/ r(a)b'^ dA , or integrating (All. 28) f(z) =iL^2iP(zi^ ;Z ^A[a-(]/b)1 ^a-1 ^^ ^ r(a)b''' Gradshteyn and Ryshik (1965) state that (All. 29) / X exp(-Mx) dx = y ^(Vjiju) where Â•Y(a,x) is the incomplete gamma function. Hence if we use (All. 29), the density of z could be rewritten as (All. 30) f(z) = -[a-d/b)]""" Y"[a,-[a-(l/b)]z]. In any given period t, vv^ith a posterior distribution on A which is gamma and an exponential random shock, we cannot get closed forms for Cbie convolution of the variables. Furthermore the "closure under sampling" property of tlie prior is lost with a prior of the form

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180 (All. 30). For instance, suppose that the prior distribution of the mean of an exponential process in any given period t is of the form (All. 30). Consider the case, now, in which new information comes from a sample of n observations from the exponential data generating process. The posterior distribution of A , determined by means of Bayes theorem, is given by (ATI. 31) -[a-(l/b)]"^ Y[a,-{a-(l/b)}A J ? [exp(-L Zx ) ] f"(Ajx) = Â— __L_JL 1 X ^ r -[a-(l/b)l"'' Y[a,-[u-(l/b)] AJV [exp(-L Ex.)]d.v t t t 1 t or (All. 32) Y[a,-{(t-(l/b) }Aj.] a" [exp(-Aj. Zx^) ] /" Y[a,-{u-(l/b)}A J a" [exp(-A^ Zx ) ] dA. t t tic Gradshteyn and Ryshik (1965) state that (All. 33) r K^^-^)e-^^ ,[v,ux] dx = Â«!il(ii.Â±^F ,1,^ + v,v + l;a/(a+e)} . where ^F (Â•) is a Gauss hypergeometric function which in most cases is indetermined. Hence, the denominator of (All. 32) cannot be determined as a closed form. Therefore, we cannot find a posterior distribution of the form of the prior distribution.

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181 Finally consider the case where samples come from an exponential process [as defined in (AII.3)]; the prior distribution for the unknown parameter is gamma [as defined in (AII.4)]; an exponential random shock is imposed on the unknown parameter [as defined in (All. 21)] and the equation that describes the nonstationary condition of the mean is given by A , ^ = A e , . We will show that even for the simplest case, where ^ t+1 t t+1 ^ the scale parameter of the gamma distribution has a value of one, we cannot get tractable results. In any given period t, assume that the posterior distribution of A is given by (All. A) and that the distribution of e is given by (Ail . 21) . If we assimie that the scale parameter has a value of one, the distribution of the product of the posterior distribution of A and tlie nonstationary random shock e ,,, i.e., z= A e .,, is given by, 3 t+1' ' t t+1 ^ ^ oo a Â— 1 (All. 34) f(z) = / {A a exp [-A-(az/A) J/AF (a) } dA , or (All. 35) f(z) = -7^ /" A^~^ exp[-A-(az/A)] dA . lU) Q We could simplify (All. 35) by using the equality (All. 19) to rewrite the integral in the equation. Hence the prior distribution of the mean at the beginning of period t+1 has a density function (All. 36) f(A_) = 2a''"^^ '^^~^^ '\ . [2^y~^/^ (a) ] ; t+1 a-1 t+1 where as before K (Â•) is a Bessel function of imaginary argument, that is

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182 (All. 37) K (2/1) = /" [exp(-2v^ (cos h)t)] (cos h)vt dt. For the same nonstationary model, if we assume that the scale parameter of the gamma distribution and the parameter of the exponential random shock are equal, say to c, then the distribution of the product of A and e is given by (All. 38) f(z) = /" {A'*"Mexp[-cA-(cz/A)]} c^"^^/Ar(a)} dA, or a+1 Â„ (All. 39) f(z) = ^ r A^"^ exp[-cA-(cz/A)] dA, or (All. 40) f(z) = 2c^"^^ z^''"^^/^ K r2cv^/r(a)]. 3. X In the case that the parameters are unrestricted, the distribution of the product of the random variables has a density (All. 41) f(z) = /" (a"^ exp[-A/b]a exp [-az/A ] /AT (a)b^} dA, or (All. 42) f(z) = -^Â— r A^-2 [-(A/b) (az/A)] dA . r(a)b^

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183 or (AII.AJ) f(z) = ZCazb)^"^"*"^^^^ K (2/uzT) /F (a)b^ . In all three cases discussed before, it is clear that the procedure does not yield tractable results. We cannot use f(z), i.e., f(A , ) Â» as the prior distribution of the unknown mean at the beginning of time period t+1 .

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APPENDIX III APPENDIX TO CHAPTER FOUR

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Al'PENDIX Hi APPENDIX TO CHAPTER FOUR Al gorithm to Determine Prediction Intervals for Lognor ma 1 and LogS tudent Distributi on s A Bayesian prediction interval of cover y is defined as an interval A such tliat (AlII.l) F(A|y) = / P(x|y) dx = Y . A In general such a prediction Interval is not unique. One particular interval which we sliall consider is defined as follows. A most plausible Bayesian prediction interval of cover Y (also called highest posterior density [H.F.D.] interval) has the form (A1II.2) A = [x:P(x|y) >_ y], where Y is determined by P(A|y) = y . If the prior distributions are natural conjugate to the process then the predictive distribution for lognormal processes is lognormal when p is unknown and o~ is known and is logStudent when y and o are both unknown. The construction of H.P.D. intervals becomes difficult for tliese distributions since they are asymmetric. In this aijpendix we develop an algoritlim to compute the prediction intervals for these distributions. If the predictive distribution is lognormal vjith mean m and variance a In, then the H.P.D. interval of cover y is of the form (a,b) where a and b are the solutions of 185

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186 (AIII.3) r fj^(x|m, a-/n) dx = y. a and among all the solutions they have the H.P.D. property. To determine the values of a and b we developed a search procedure. If the predictive distribution is logStudent then the H.P.D. interval of cover y is of the form (a,b) where a and b are the solution of (AIII.4) / ^TQ^^I"*' "' ^' '^^ '^^ " Y > such that the H.P.D. property holds. Suppose that the predictive distribution could be represented as in Figure AIlI.l f(x) Mode "igure AIII.l Predictive Distribution

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187 The search procedure works as follows: (i) in the first iteration lahe 1 the value of the density function at the mode value A. i.e., A= f(Mode); label the value of the density function at the origin C, i.e., C = f(0). Select an arbitrary initial point a (greater than the mode) and find another point a, with equal density. (See Figure Mil. 2.) The value of the density function for this initial value will be between points A and C; label it B, i.e., B = f(a^) = f(a2). f(x)

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188 the density function for the next point in the search will be between points B and C. Relabel those points as A = B and C = C; then select the next point in the search. (See Figure AIII.3.) f(x) a. Mode Figure A1II.3 Predictive Distribution b) If / f(x) dx > Y then it means that the value ^1 of the density function corresponding to the next point in the search will be between points A and B. Relabel those points as A = A and

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189 C = B ; hen select the next jxiint in the search. (See Figure AIII.4.) f(x) a-. Mode Figure AIII.4 Predictive Distribution (iii) To select the new points for a and a^, in either cases K ^ 2' ii-a or ii-b, take a to be the solution to -the following equation (AIII.5) f (a,) = C + .681 ( A C ) See lAu-iiberger (1973) for a discussion of the use of the golden section method, which usl-s the constant .681 .

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190 and then lind d,^ with deusiLy equal to a , where t (Â•) is the predictive density functioci. (iv) Once we find a and a (with equal density) we could go to step (ii) and repeat the procedure.. The algorithm stops if it does not find the desired y content intervals within a specified number of iterations or if it finds the interval for a specified precision, that is if the absolute value of the difference between the computed y content and the required y content does not exceed a specified precision. A computer program was written to determine the H.P.D. Intervals for lognormal and logStudent distributions using the previous algorithm. The computational work requires the use of some numerical algorithms. We used three computer packages from the International Mathematical and Statistical hibraries, Inc. Volume 2. To determine the mode of the lognormal and logStudent distributions we used the subroutine ZXMIN, which is a quasi-Newton algorithm for finding the minimum of a function of N variables. To integrate the functions from a to a^ we used DCADRE , which integrates a function t(x) from a to b using cautious adaptive Romberg extrapolation. To determine the new values of a, and a^, say a* and a^ , we used the subroutine ZREALl, which finds real zeros of a real function f(x) where the initial guesses may not be good. In Tables 1 and 2 we present some intervals computed for some lognormal and logStudent distributions.

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192 TABLE 2 PREDICTIVE INTERVALS FOR SOME LOGSTUDENT PREDICTIVE DISTRIBUTIONS Lower Upper Computed Density PARAf-IETERS Interval Limit: Limit: interval: at the number of m V n d Y a^. ^9 '^* Limits iterations 2 .4 15 10 .90 .2989 22.2919 .8999 .0083 14 2 .5 14 10 .90 .4940 20.7564 .8999 .0094 13 2 .5 15 9 .90 .4637 20.5035 .8999 .0097 12 2 .5 15 10 .80 .97384 14.4370 .7999 .0237 12 2 .5 15 10 .90 .5481 19.7881 .9001 .0107 10 2 .5 15 10 .95 .2237 29.3595 .9499 .0034 16 2 .5 15 11 .90 .5619 20.2267 .8999 .0101 11 2 .5 16 10 ,90 .5334 20.3135 .8999 .0099 14 2 .6 15 10 .90 .8561 18.4218 .8999 .0121 14 3 .5 15 10 .90 1.4081 55.3166 .8999 .0036 13

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iy3 The algorithm is used to determine highest posterior density intervals but can be used to determine any type of intervals desired. Minor changes in the computer program are needed to determine one sided prediction intervals or any other interval needed. To get any of the intervals shown in Tables 1 and 2, the user needs to submit only the parameters of the predictive distribution, the desired y content of the interval and the value of the complete Beta function, B{l/2,(d/2)} . The computer program then gives as the output all the information that appears in Tables 1 and 2. For instance, when the shift rate, y, and the spread parameter, o^, of a lognormal predictive distribution are 1 and .5 respectively and a .90 content interval is desired, the algorithm finds a .8999 content interval with limits .3988 and 6.8162. Similarly when the parameters of a logStudent predictive distribution are (m=2, v=.A, n=15, d=10) and a .90 content interval is required the algorithm finds a .8999 content interval with limits .2989 and 22.2919. Following we present a computer printout of the program to find predictive intervals for the logStudent distribution.

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194 I y J .' Jr I ^< \t \< \i i ^ jt M , . n . ~ ^ y J . Â— 9 < Â— -. r<< 1 < <. -Â•7^' .1 ^ a o 1 ; >Â• J -.Â— '>, J M Z C '" -3 3 -

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195 y Â» J > 1 d -."Â•< . 1 Â— o Â— Â— Â• Â• Â• .T 3 > J3 c n Â— *

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196 'Â•J' /I n J" /Â» 'Â•-* J r a ^ Â» .1 T 71 i -Nj J ' i T O l^^ Jiiw-vj Â— > Â» a M -. It

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197 Â— J

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212 173. Winkler, R.L. and C.B. Barry (1973). "Nonstationary Means In a Multinomal Process". Re search Repor t , RR-73-9, Laxemburg, Austria: International Institute for Applied Systems Analysis. 17^. Yuan, P. (1933). "On the Logarithmic Frequency Distribution and the Semi-Logarithmic Correlation Surface" . Annals of Mathematical Statistics , Vol, 4, pp. 30-74. 175. Zellner, A. (1971). An Introducti o n to Bayesian Inference in Econometric s . New York: Wiley. 176. (1971). "Bayesian and Non-Bayesian Analysis of the Log-Normal Distribution and Log-Normal Regression", Journal of t he American Statistical Association , Vol. 66. pp. 327-330.

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213 BIOGRAPHTCAJ. SKETCH Jorge Ivan Velez-Arocho was born in Lares, Puerto Rico, on October 25, 1947, son of Jorge Velez Velez and Elba Lucrecia Arocho Velez. He attended elementary and second.iry school in tlie Lares, Puerto Rico public schoo] system. He graduated from Domingo Aponte Collazo High School, Lares-Puerto Rico in 1966. From 1966 tlnrinigh 1969 he attended the University of Puerto Rico (U.P.R.), at Rio Piedras. His university attendanc-e was interrupted by active service in the United States National Guard fri^)m June 1969 to May 1970. In May 25, 1970 he graduated as Mc^dical Corpsinan from the Medical Training Center of the U.S. Army at Fort Sam Houston, Texas. Upon his return to Puerto Rico he joijied the Puerto Rico National Guard where he served for four years as Senior Medical Aidman . He returned to the U.P.R. where he received liis B.iS.A. degree in December 1970 with a major in Quantitative Methods. He received his M.B.A. degree from the Graduate School of Business Administration of the U.P.R. in June 1973 with a major in Quantitative Methods. While at the Graduate School he held the positions of computer laboratory assistant and graduate teaching assistant. In July 1972 he joined the faculty of the School of Business Administration of the U.P.R. at Mayaguez where he tield I lie rank of instructor and taught courst-s on quantitative methods for management and introductory statistics. He received a leave of absence from the U.P.R.

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214 in 1974 and entered tlie Oradiiate School of the University of Florida to pursue the degree of Doctor of Philosophy, In that year he joined the 301st Field Hospital of the U.S. Army Reserve where he served as Senior Clinical Specialist. He was honorably discharged from the Armed Forces of the United States in May 1975. From September 1974 to July 1977 he v^as employed as a Research Assistant in the Department of Management of the University of Florida, During the Summer of 1976 he taught a course on quantitative methods for managers at the Department of Management of that University. Since September 1977 he has been employed as a Research Associate in the Department of Finance of the University of Texas at Austin, He was married to Digna de los Angeles Hernandez on May 26,1973 and they are the proud parents of a daughter; Angeles Maria,

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1 certify that I have read this stiuiy and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. Christopfier B. Barry Associate Professor of Manasjenient I certify that I have read this study and that in my opinion it conform to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. Thomas Hodgsc Associate Professor of Industrial Engineering I certify that I have read this study and that in my opinion it conform to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. Antal Ma j thay Associate Professor of Management I certify that I have read this study and that in my opinion it conform to acceptable standards of scholarly presentation and is fully adequate, in sco|je and i|uality, as a lii ssertation for tlie degree of Doctor of Pli i I osuijIiv . ^ <:L. ) h . /'C, (. Zoran Pop-Stoianovic Professor of Mathematics

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I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. Gary Kbehler Associate Professor of Management This dissertation was submitted to the Graduate Faculty of the Department of Management in the College of Business Administration and to the Graduate Council, and was accepted as partial fulfillment of the requirements for the degree of Doctor of Philosophy. June 1978 Dean, Graduate School

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