Abstract
We present a Bayesian variable selection method based on an extension of the Zellner’s -prior in linear models. More specifically, we propose a two-component -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 -prior. Simulation results also indicate that models selected using the method with the two-component -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.
Citation
Hongmei Zhang. Xianzheng Huang. Jianjun Gan. Wilfried Karmaus. Tara Sabo-Attwood. "A Two-Component -Prior for Variable Selection." Bayesian Anal. 11 (2) 353 - 380, June 2016. https://doi.org/10.1214/15-BA953
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