Title of article
Bayesian likelihood robustness in linear models
Author/Authors
Peٌa، نويسنده , , Daniel and Zamar، نويسنده , , Ruben and Yan، نويسنده , , Guohua، نويسنده ,
Issue Information
روزنامه با شماره پیاپی سال 2009
Pages
12
From page
2196
To page
2207
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.
Keywords
Kullback–Leibler divergence , robust regression , Bayesian inference , Heteroscedasticity
Journal title
Journal of Statistical Planning and Inference
Serial Year
2009
Journal title
Journal of Statistical Planning and Inference
Record number
2220067
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