CRITERIA FOR BAYESIAN MODEL CHOICE WITH APPLICATION TO VARIABLE SELECTION

• 成果类型: Article
• 署名作者: Bayarri, M. J.; Berger, J. O.; Forte, A.; Garcia-Donato, G.
• 署名单位: University of Valencia; Duke University; Universitat Jaume I; Universidad de Castilla-La Mancha
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/12-AOS1013
• 发表日期: 2012
• 页码: 1550-1577
• 关键词: priors; Consistency; hypotheses

摘要

In objective Bayesian model selection, no single criterion has emerged as dominant in defining objective prior distributions. Indeed, many criteria have been separately proposed and utilized to propose differing prior choices. We first formalize the most general and compelling of the various criteria that have been suggested, together with a new criterion. We then illustrate the potential of these criteria in determining objective model selection priors by considering their application to the problem of variable selection in normal linear models. This results in a new model selection objective prior with a number of compelling properties.