 Permanent Link:
 https://ufdc.ufl.edu/UF00097429/00001
Material Information
 Title:
 Continuously adaptive Mestimation in the linear model
 Added title page title:
 Mestimation in the linear model
 Creator:
 Conlon, Michael, 1953 ( Dissertant )
Khuri, Andre I. ( Thesis advisor )
Hearn, Donald W. ( Reviewer )
Littel, Ramon C. ( Reviewer )
 Place of Publication:
 Gainesville, Fla.
 Publisher:
 University of Florida
 Publication Date:
 1982
 Copyright Date:
 1982
 Language:
 English
 Physical Description:
 x, 171 leaves : ill. ; 28 cm.
Subjects
 Subjects / Keywords:
 Approximation ( jstor )
Density estimation ( jstor ) Estimation methods ( jstor ) Estimators ( jstor ) Least squares ( jstor ) Mathematical robustness ( jstor ) Maximum likelihood estimations ( jstor ) Outliers ( jstor ) Statistical discrepancies ( jstor ) Statistics ( jstor ) Dissertations, Academic  Statistics  UF Estimation theory ( lcsh ) Linear models (Statistics) ( lcsh ) Statistics thesis Ph. D
 Genre:
 bibliography ( marcgt )
nonfiction ( marcgt )
Notes
 Abstract:
 Mestimates of regression parameters are found by minimizing the sum
of a function of the difference between observed and predicted values of
a dependent variable. The choice of a particular function before the data
have been examined is shown to have serious consequences for the asymptotic
variance of the parameter estimates. Previous adaptive Mestimates used
one of a small number of functions selected after preliminary examination
of the data.
Continuously adaptive Mestimation (CAM) is introduced to choose a
function according to maximum likelihood criteria from a continuous class
of functions, thereby simultaneously estimating the regression parameters
and the underlying error density. Algorithms for calculating the estimates
are derived and numerical examples demonstrate the method's performance in
a variety of regression problems, including symmetric and asymmetric errors,
 Thesis:
 Thesis (Ph. D.)University of Florida, 1982.
 Bibliography:
 Includes bibliographic references (leaves 164170).
 General Note:
 Typescript.
 General Note:
 Vita.
 Statement of Responsibility:
 by Michael Conlon.
Record Information
 Source Institution:
 University of Florida
 Holding Location:
 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 nonprofit 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:
 000317575 ( alephbibnum )
08835244 ( oclc ) ABU4400 ( notis )
