Convergence analysis of block Gibbs samplers for Bayesian linear mixed models with $p>N$

Abstract

Exploration of the intractable posterior distributions associated with Bayesian versions of the general linear mixed model is often performed using Markov chain Monte Carlo. In particular, if a conditionally conjugate prior is used, then there is a simple two-block Gibbs sampler available. Román and Hobert [Linear Algebra Appl. 473 (2015) 54-77] showed that, when the priors are proper and the $X$ matrix has full column rank, the Markov chains underlying these Gibbs samplers are nearly always geometrically ergodic. In this paper, Román and Hobert's (2015) result is extended by allowing improper priors on the variance components, and, more importantly, by removing all assumptions on the $X$ matrix. So, not only is $X$ allowed to be (column) rank deficient, which provides additional flexibility in parameterizing the fixed effects, it is also allowed to have more columns than rows, which is necessary in the increasingly important situation where $p>N$. The full rank assumption on $X$ is at the heart of Román and Hobert's (2015) proof. Consequently, the extension to unrestricted $X$ requires a substantially different analysis.

Published at http://dx.doi.org/10.3150/15-BEJ749 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)

References in corpus (4)

• Markov Chain Monte Carlo: Can We Trust the Third Significant Figure?
• A spectral analytic comparison of trace-class data augmentation algorithms and their sandwich variants
• Convergence analysis of the Gibbs sampler for Bayesian general linear mixed models with improper priors
• Gibbs Sampling, Exponential Families and Orthogonal Polynomials

Cited by in corpus (1)

• Estimating MCMC convergence rates using common random number simulation