Title

A note on parameter and standard error estimation in adaptive robust regression

Authors

Authors

M. Jamshidian

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

J. Stat. Comput. Simul.

Keywords

EM algorithm; iterative reweighting; normal/independent family; regression; SAS NLIN; t distribution; slash distribution; T-DISTRIBUTION; EM ALGORITHM; Computer Science, Interdisciplinary Applications; Statistics &; Probability

Abstract

This paper introduces practical methods of parameter and standard error estimation for adaptive robust regression where errors are assumed to be from a normal/independent family of distributions. In particular, generalized EM algorithms (GEM) are considered for the two cases of t and slash families of distributions. For the t family, a one step method is proposed to estimate the degree of freedom parameter. Use of empirical information is suggested for standard error estimation. It is shown that this choice leads to standard errors that can be obtained as a by-product of the GEM algorithm. The proposed methods, as discussed, can be implemented in most available nonlinear regression programs. Details of implementation in SAS NON are given using two specific examples.

Journal Title

Journal of Statistical Computation and Simulation

Volume

71

Issue/Number

1

Publication Date

1-1-2001

Document Type

Article

Language

English

First Page

11

Last Page

27

WOS Identifier

WOS:000173834600002

ISSN

0094-9655

Share

COinS