Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1149570 | Journal of Statistical Planning and Inference | 2009 | 12 Pages |
Abstract
This 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.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Daniel Peña, Ruben Zamar, Guohua Yan,