A study of variable selection using g-prior distribution with ridge parameter

In the Bayesian stochastic search variable selection framework, a common prior distribution for the regression coefficients is the g-prior of Zellner [1986]. However, there are two standard cases in which the associated covariance matrix does not exist, and the conventional prior of Zellner can not be used: if the number of observations is lower than the number of variables (large p and small n paradigm), or if some variables are linear combinations of others. In such situations a prior distribution derived from the prior of Zellner can be used, by introducing a ridge parameter. This prior introduced by Gupta and Ibrahim [2007] is a flexible and simple adaptation of the g-prior. In this paper we study the influence of the ridge parameter on the selection of variables. A simple way to choose the associated hyperparameters is proposed. The method is valid for any generalized linear mixed model and we focus on the case of probit mixed models when some variables are linear combinations of others. The method is applied to both simulated and real datasets obtained from Affymetrix microarray experiments. Results are compared to those obtained with the Bayesian Lasso.