Bayesian Variable Selection in Regression

摘要

This paper is concerned with the selection of subsets of ''predictor'' variables in a linear regression model for the prediction of a ''dependent'' variable. We take a Bayesian approach and assign a probability distribution to the dependent variable through a specification of prior distributions for the unknown parameters in the regression model. The appropriate posterior probabilities are derived for each submodel and methods are proposed for evaluating the family of prior distributions. Examples are given that show the application of the Bayesian methodology. 23 refs., 3 figs. (ERA citation 12:015248)