Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 12, Number 2 (2018), 4412-4439.
Convergence analysis of the block Gibbs sampler for Bayesian probit linear mixed models with improper priors
In this article, we consider Markov chain Monte Carlo (MCMC) algorithms for exploring the intractable posterior density associated with Bayesian probit linear mixed models under improper priors on the regression coefficients and variance components. In particular, we construct a two-block Gibbs sampler using the data augmentation (DA) techniques. Furthermore, we prove geometric ergodicity of the Gibbs sampler, which is the foundation for building central limit theorems for MCMC based estimators and subsequent inferences. The conditions for geometric convergence are similar to those guaranteeing posterior propriety. We also provide conditions for the propriety of posterior distributions with a general link function when the design matrices take commonly observed forms. In general, the Haar parameter expansion for DA (PX-DA) algorithm is an improvement of the DA algorithm and it has been shown that it is theoretically at least as good as the DA algorithm. Here we construct a Haar PX-DA algorithm, which has essentially the same computational cost as the two-block Gibbs sampler.
Electron. J. Statist., Volume 12, Number 2 (2018), 4412-4439.
Received: November 2017
First available in Project Euclid: 18 December 2018
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 60J05: Discrete-time Markov processes on general state spaces
Secondary: 62F15: Bayesian inference
Wang, Xin; Roy, Vivekananda. Convergence analysis of the block Gibbs sampler for Bayesian probit linear mixed models with improper priors. Electron. J. Statist. 12 (2018), no. 2, 4412--4439. doi:10.1214/18-EJS1506. https://projecteuclid.org/euclid.ejs/1545123629