文章基本信息

标题：A Two-Component $G$-Prior for Variable Selection
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作者：Hongmei Zhang ; Xianzheng Huang ; Jianjun Gan 等
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2016
卷号：11
期号：2
页码：353-380
DOI：10.1214/15-BA953
语种：English
出版社：International Society for Bayesian Analysis
摘要：We present a Bayesian variable selection method based on an extension of the Zellner’s g-prior in linear models. More specifically, we propose a two-component G-prior, wherein a tuning parameter, calibrated by use of pseudo-variables, is introduced to adjust the distance between the two components. We show that implementing the proposed prior in variable selection is more efficient than using the Zellner’s g-prior. Simulation results also indicate that models selected using the method with the two-component G-prior are generally more favorable with smaller losses compared to other methods considered in our work. The proposed method is further demonstrated using our motivating gene expression data from a lung disease study, and ozone data analyzed in earlier studies.