Mixtures of g priors for Bayesian variable selection

• 成果类型: Review
• 署名作者: Liang, Feng; Paulo, Rui; Molina, German; Clyde, Merlise A.; Berger, Jim O.
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; Universidade de Lisboa; Duke University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214507000001337
• 发表日期: 2008
• 页码: 410-423
• 关键词: approximations; regression; matrix

摘要

Zellner's g prior remains a popular conventional prior for use in Bayesian variable selection, despite several undesirable consistency issues. In this article we study mixtures of g priors as an alternative to default g priors that resolve many of the problems with the original formulation while maintaining the computational tractability that has made the g prior so popular. We present theoretical properties of the mixture g priors and provide real and simulated examples to compare the mixture formulation with fixed g priors, empirical Bayes approaches, and other default procedures.