Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Eva Cantoni
Title: Analysis of Robust Quasi-deviances for Generalized Linear Models

Generalized linear models (McCullagh and Nelder 1989) are a popular technique for modeling a large variety of continuous and discrete data. They assume that the response variables Yi , for i = 1, . . . , n, come from a distribution belonging to the exponential family, such that E[Yi ] = µi and V[Yi ] = V (µi ), and that ηi = g(µi ) = xiTβ, where β ∈ IR p is the vector of parameters, xi ∈ IR p, and g(.) is the link function.

The non-robustness of the maximum likelihood and the maximum quasi-likelihood estimators has been studied extensively in the literature. For model selection, the classical analysis-of-deviance approach shares the same bad robustness properties. To cope with this, Cantoni and Ronchetti (2001) propose a robust approach based on robust quasi-deviance functions for estimation and variable selection. We refer to that paper for a deeper discussion and the review of the literature.

Page views:: 9107. Submitted: 2002-11-07. Published: 2004-04-26.
Paper: Analysis of Robust Quasi-deviances for Generalized Linear Models     Download PDF (Downloads: 8342)
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DOI: 10.18637/jss.v010.i04

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