Mixtures of g Priors for Bayesian Variable Selection
作者： Feng Liang , Rui Paulo , German Molina , Merlise A Clyde , Jim O Berger
DOI: 10.1198/016214507000001337
关键词: Mathematical optimization 、 Decision theory 、 g-prior 、 Model selection 、 Mathematics 、 Bayes' theorem 、 Prior probability 、 Consistency (statistics) 、 Econometrics 、 Feature selection 、 Cauchy distribution
摘要: Zellner's g prior remains a popular conventional for use in Bayesian variable selection, despite several undesirable consistency issues. In this article we study mixtures of priors as an alternative to default that resolve many the problems with original formulation while maintaining computational tractability has made so popular. We present theoretical properties mixture and provide real simulated examples compare fixed priors, empirical Bayes approaches, other procedures. Please see Arnold letter author's response.
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