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

    Authors

    M. Jamshidian

    Comments

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    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

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