Bayesian Variable Selection with Hierarchical Priors
Description
Bayesian variable selection for linear regression models using hierarchical
priors. There is a prior that combines information across responses and one
that combines information across covariates, as well as a standard spike and
slab prior for comparison. An MCMC samples from the marginal posterior
distribution for the 0-1 variables indicating if each covariate belongs to the
model for each response.