Slice sampling for simulation based fitting of spatial data models
Statistics and Computing
Bayesian analysis of the unobserved ARCH model
Statistics and Computing
Market Roll-Out and Retailer Adoption for New Brands
Marketing Science
Bayesian analysis of two dependent 2×2 contingency tables
Computational Statistics & Data Analysis
Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach
Computational Statistics & Data Analysis
A space-time filter for panel data models containing random effects
Computational Statistics & Data Analysis
Parallel multivariate slice sampling
Statistics and Computing
Journal of Multivariate Analysis
A Bayesian model for longitudinal circular data based on the projected normal distribution
Computational Statistics & Data Analysis
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Markov chain Monte Carlo (MCMC) algorithms have revolutionized Bayesian practice. In their simplest form (i.e., when parameters are updated one at a time) they are, however, often slow to converge when applied to high-dimensional statistical models. A remedy for this problem is to block the parameters into groups, which are then updated simultaneously using either a Gibbs or Metropolis-Hastings step. In this paper we construct several (partially and fully blocked) MCMC algorithms for minimizing the autocorrelation in MCMC samples arising from important classes of longitudinal data models. We exploit an identity used by Chib (1995) in the context of Bayes factor computation to show how the parameters in a general linear mixed model may be updated in a single block, improving convergence and producing essentially independent draws from the posterior of the parameters of interest. We also investigate the value of blocking in non-Gaussian mixed models, as well as in a class of binary response data longitudinal models. We illustrate the approaches in detail with three real-data examples.