Bayesian likelihood robustness in linear models

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  1. Department of Statistics
  2. Bayesian likelihood robustness in linear models

Bayesian likelihood robustness in linear models

TitleBayesian likelihood robustness in linear models
Publication TypeJournal Article
Year of Publication2009
AuthorsPena, D, Zamar, R, Yan, G
JournalJOURNAL OF STATISTICAL PLANNING AND INFERENCE
Volume139
Pagination2196-2207
Date PublishedJUL 1
Type of ArticleArticle
ISSN0378-3758
KeywordsBayesian inference, Heteroscedasticity, Kullback-Leibler divergence, Robust regression
AbstractThis paper deals with the problem of robustness of Bayesian regression with respect to the data. We first give a formal definition of Bayesian robustness to data contamination, prove that robustness according to the definition cannot be obtained by using heavy-tailed error distributions in linear regression models and propose a heteroscedastic approach to achieve the desired Bayesian robustness. (C) 2008 Elsevier B.V. All rights reserved.
DOI10.1016/j.jspi.2008.10.012