Bayesian Likelihood Robustness in Linear Models Articles uri icon

authors

  • PE√ĎA GIL, DANIEL
  • ZAMAR, RUBEN HORACIO
  • YAN, GUOHUA

publication date

  • July 2009

start page

  • 2196

end page

  • 2207

issue

  • 7

volume

  • 139

International Standard Serial Number (ISSN)

  • 0378-3758

Electronic International Standard Serial Number (EISSN)

  • 1873-1171

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.