130 – Bayesian Variable Selection
Bayesian Variable Selection for Median Latent Variable Model
Yifan Wang
Capital University of Economics and Business
Zhibin Xu
Capital University of Economics and Business
Hong Ji
Capital University of Economics and Business
In biomedical, psychological, social, and behavioral sciences, it is very common to encounter latent variables along with non-normal data. We propose a median latent variable model to deal with this kind of data in a Bayesian framework. The normal-gamma prior distribution is applied here for simultaneous estimation and model selection. A Markov chain Monte Carlo (MCMC) algorithm for obtaining Bayesian estimates is developed. Simulation studies are carried out to examine the finite sample performance of the proposed estimators. We illustrate the proposed method with a real data set from a longitudinal study of polydrug use.