FULLY BAYES FACTORS WITH A GENERALIZED g-PRIOR

• 成果类型: Article
• 署名作者: Maruyama, Yuzo; George, Edward I.
• 署名单位: University of Tokyo; University of Pennsylvania
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/11-AOS917
• 发表日期: 2011
• 页码: 2740-2765
• 关键词: ridge-regression estimators; variable selection; MODEL; Lasso

摘要

For the normal linear model variable selection problem, we propose selection criteria based on a fully Bayes formulation with a generalization of Zellner's g-prior which allows for p > n. A special case of the prior formulation is seen to yield tractable closed forms for marginal densities and Bayes factors which reveal new model evaluation characteristics of potential interest.