A Two-Component G Prior for Variable Selection

• Activity Number: 130
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Hongmei Zhang*+ and Jianjun Gan and Wilfried Karmaus and Tara Sabo-Attwood
• Companies: University of Memphis and GlaxoSmithKline and University of Memphis and University of Florida
• Keywords: Prediction loss ; Pseudo variables ; Tunning parameter ; Measurement error

Abstract

A Bayesian variable selection method based on an extension of the Zellner's g-prior in linear models is presented. We show that implementing the proposed prior in variable selection is more efficient than using the Zellner's g-prior. Simulation results indicate that models selected using the new method are generally more favorable with smaller prediction 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.