@article { ISI:000265170600008,
	title = {Bayesian likelihood robustness in linear models},
	journal = {JOURNAL OF STATISTICAL PLANNING AND INFERENCE},
	volume = {139},
	number = {7},
	year = {2009},
	month = {JUL 1},
	pages = {2196-2207},
	publisher = {ELSEVIER SCIENCE BV},
	type = {Article},
	address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS},
	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. (C) 2008 Elsevier B.V. All rights reserved.},
	keywords = {Bayesian inference, Heteroscedasticity, Kullback-Leibler divergence, Robust regression},
	issn = {0378-3758},
	doi = {10.1016/j.jspi.2008.10.012},
	author = {Pena, Daniel and Zamar, Ruben and Yan, Guohua}
}
