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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Bayesian robustness under a skew-normal class of prior distribution

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Author(s):
de Godoi, Luciana Graziela [1] ; Branco, Marcia D'Elia [2]
Total Authors: 2
Affiliation:
[1] Univ Fed Espirito Santo, Dept Estat, Goiabeiras, Vitoria - Brazil
[2] Univ Sao Paulo, Dept Estat, BR-05508 Sao Paulo - Brazil
Total Affiliations: 2
Document type: Journal article
Source: INTERNATIONAL JOURNAL OF APPROXIMATE REASONING; v. 55, n. 5, p. 1235-1251, JUL 2014.
Web of Science Citations: 0
Abstract

We develop a global sensitivity analysis to measure the robustness of the Bayesian estimators with respect to a class of prior distributions. This class arises when we consider multiplicative contamination of a base prior distribution. A similar structure was presented by van der Linde {[}12]. Some particular specifications for this multiplicative contamination class coincide with well known families of skewed distributions. In this paper, we explore the skew-normal multiplicative contamination class for the prior distribution of the location parameter of a normal model. Results of a Bayesian conjugation and expressions for some measures of distance between posterior means and posterior variance are obtained. We also elaborate on the behavior of the posterior means and of the posterior variances through a simulation study. (C) 2014 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 04/15304-6 - Regression models and applications
Grantee:Heleno Bolfarine
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 12/21788-2 - Regression models and applications
Grantee:Heleno Bolfarine
Support Opportunities: Research Projects - Thematic Grants