Necessary and Sufficient Conditions for High-Dimensional Posterior Consistency under $g$-Priors
Statistics Theory
2015-09-04 v1 Statistics Theory
Abstract
We examine necessary and sufficient conditions for posterior consistency under -priors, including extensions to hierarchical and empirical Bayesian models. The key features of this article are that we allow the number of regressors to grow at the same rate as the sample size and define posterior consistency under the sup vector norm instead of the more conventional Euclidean norm. We consider in particular the empirical Bayesian model of George and Foster (2000), the hyper--prior of Liang et al. (2008), and the prior considered by Zellner and Siow (1980).
Keywords
Cite
@article{arxiv.1509.01060,
title = {Necessary and Sufficient Conditions for High-Dimensional Posterior Consistency under $g$-Priors},
author = {Douglas K. Sparks and Kshitij Khare and Malay Ghosh},
journal= {arXiv preprint arXiv:1509.01060},
year = {2015}
}
Comments
Published at http://dx.doi.org/10.1214/14-BA893 in the Bayesian Analysis (http://projecteuclid.org/euclid.ba) by the International Society of Bayesian Analysis (http://bayesian.org/)