Bayesian Variable Selection in Regression Models using The Laplace Approximation.

Abstract

The Bayesian variable selection analysis is widely used as a new methodology in air quality control trials and generalized linear models. One of the important and, of course,<br /> controversial topics in this area is selection of prior distribution of unknown model parameters. The aim of this study is presenting a substitution for mixture of priors which besides<br /> preservation of benefits and computational efficiencies obviate the available paradoxes and<br /> contradictions. In this research we pay attention to two points of view; empirical and fully<br /> Bayesian. Especially, a mixture of priors and its theoretical characteristics is introduced.<br /> Finally, the proposed model is illustrated with a real example.