Abstract
Biometrika, vol. 100, no. 4, pp. 1011-1018, Dec. 2013 We investigate the asymptotic behavior of posterior distributions of
regression coefficients in high-dimensional linear models as the number of
dimensions grows with the number of observations. We show that the posterior
distribution concentrates in neighborhoods of the true parameter under simple
sufficient conditions. These conditions hold under popular shrinkage priors
given some sparsity assumptions.