Title

A Note On Parameter And Standard Error Estimation In Adaptive Robust Regression

Keywords

Em algorithm; Iterative reweighting; Normal/independent family; Regression; SAS NLIN; Slash distribution; T distribution

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 NLIN are given using two specific examples.

Publication Date

1-1-2001

Publication Title

Journal of Statistical Computation and Simulation

Volume

71

Issue

1

Number of Pages

11-27

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1080/00949650108812131

Socpus ID

0035647894 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0035647894

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