Posterior consistency in linear models under shrinkage priors
Methodology
2018-03-06 v4 Statistics Theory
Statistics Theory
Abstract
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.
Cite
@article{arxiv.1104.4135,
title = {Posterior consistency in linear models under shrinkage priors},
author = {Artin Armagan and David B. Dunson and Jaeyong Lee and Waheed U. Bajwa and Nate Strawn},
journal= {arXiv preprint arXiv:1104.4135},
year = {2018}
}
Comments
To appear in Biometrika