A two-component G-Prior for variable selection
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 pseudovariables, 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.
Publication Title
Bayesian Analysis
Recommended Citation
Zhang, H., Huang, X., Gan, J., Karmaus, W., & Sabo-Attwood, T. (2016). A two-component G-Prior for variable selection. Bayesian Analysis, 11 (2), 353-380. https://doi.org/10.1214/15-BA953
