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
Link To Document :
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