Robust Markov chain Monte Carlo methods for spatial generalized linear mixed models

Using Markov chain Monte Carlo methods for statistical inference is often troublesome in practice, because performance of the algorithm may hugely depend on the observed data, and what works well for one dataset may fail miserably for another. In this article, for spatial generalized linear mixed models (GLMMs), we discuss problems with algorithms previously used, and we construct an algorithm with robust mixing and convergence characteristics, independent of the data. The strategy we have used for this construction is not model specific and could be applied in a much wider context.