文章基本信息

标题：Geometric ergodicity of Gibbs samplers for the Horseshoe and its regularized variants
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作者：Suman Bhattacharya ; Kshitij Khare ; Subhadip Pal 等
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2022
卷号：16
期号：1
页码：1-57
DOI：10.1214/21-EJS1932
语种：English
出版社：Institute of Mathematical Statistics
摘要：The Horseshoe is a widely used and popular continuous shrinkage prior for high-dimensional Bayesian linear regression. Recently, regularized versions of the Horseshoe prior have also been introduced in the literature. Various Gibbs sampling Markov chains have been developed in the literature to generate approximate samples from the corresponding intractable posterior densities. Establishing geometric ergodicity of these Markov chains provides crucial technical justification for the accuracy of asymptotic standard errors for Markov chain based estimates of posterior quantities. In this paper, we establish geometric ergodicity for various Gibbs samplers corresponding to the Horseshoe prior and its regularized variants in the context of linear regression. First, we establish geometric ergodicity of a Gibbs sampler for the original Horseshoe posterior under strictly weaker conditions than existing analyses in the literature. Second, we consider the regularized Horseshoe prior introduced in [18], and prove geometric ergodicity for a Gibbs sampling Markov chain to sample from the corresponding posterior without any truncation constraint on the global and local shrinkage parameters. Finally, we consider a variant of this regularized Horseshoe prior introduced in [15], and again establish geometric ergodicity for a Gibbs sampling Markov chain to sample from the corresponding posterior.
关键词：60J05;60J20;geometric ergodicity;high-dimensional linear regression;horseshoe prior;Markov chain Monte Carlo