Bayesian Subset Selection for Inverse Gauss Regression Models

Abstract

Inspired by the idea of Kuo and Mallick, Bayesian subset selection for inverse Gauss regression models is studied by Gibbs sampler and Metropolis-Hastings algorithm in this paper. Simulation study and the aerobic fitness data example are employed to demonstrate the proposed methodology.