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June 2016 A Two-Component G-Prior for Variable Selection
Hongmei Zhang, Xianzheng Huang, Jianjun Gan, Wilfried Karmaus, Tara Sabo-Attwood
Bayesian Anal. 11(2): 353-380 (June 2016). DOI: 10.1214/15-BA953

Abstract

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.

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Hongmei Zhang. Xianzheng Huang. Jianjun Gan. Wilfried Karmaus. Tara Sabo-Attwood. "A Two-Component G-Prior for Variable Selection." Bayesian Anal. 11 (2) 353 - 380, June 2016. https://doi.org/10.1214/15-BA953

Information

Published: June 2016
First available in Project Euclid: 5 May 2015

zbMATH: 1357.62249
MathSciNet: MR3471994
Digital Object Identifier: 10.1214/15-BA953

Rights: Copyright © 2016 International Society for Bayesian Analysis

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Vol.11 • No. 2 • June 2016
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