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