Modeling spatial dependence in finite mixture models for disease mapping

A vast literature has recently been concerned with the analysis of variation in multivariate counts recorded across geographical areas with the aim of detecting clusters of regions with homogeneous behavior. Most of the modeling approaches have been discussed for the univariate case and only very recently spatial models have been extended to predict more than one outcome simultaneously. We extend standard finite mixture models to the analysis of multiple, spatially correlated, counts. Dependence among outcomes is modeled using a set of of correlated random effects, while the spatial structure is captured by the use of a Gibbs representation for the prior probabilities of component membership.