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2022 Geometric ergodicity of Gibbs samplers for the Horseshoe and its regularized variants
Suman Bhattacharya, Kshitij Khare, Subhadip Pal
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Electron. J. Statist. 16(1): 1-57 (2022). DOI: 10.1214/21-EJS1932

Abstract

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.

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Suman Bhattacharya. Kshitij Khare. Subhadip Pal. "Geometric ergodicity of Gibbs samplers for the Horseshoe and its regularized variants." Electron. J. Statist. 16 (1) 1 - 57, 2022. https://doi.org/10.1214/21-EJS1932

Information

Received: 1 January 2021; Published: 2022
First available in Project Euclid: 6 January 2022

Digital Object Identifier: 10.1214/21-EJS1932

Subjects:
Primary: 60J05
Secondary: 60J20

Keywords: geometric ergodicity , high-dimensional linear regression , horseshoe prior , Markov chain Monte Carlo

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Vol.16 • No. 1 • 2022
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