Comparing Bayesian Spatial Conditional Overdispersion and the Besag–York–Mollié Models: Application to Infant Mortality Rates

In this paper, we review overdispersed Bayesian generalized spatial conditional count data models. Their usefulness is illustrated with their application to infant mortality rates from Colombian regions and by comparing them the widely used Besag–York–Mollié (BYM) These models assume that excess of dispersion in may be partially caused possible dependence existing among different units. Thus, specific regression structures are then proposed both for mean parameter models, including covariates, as well an assumed neighborhood structure. We focus on case response variables following a Poisson distribution, specifically concentrating normal overdispersion model. Models were fitted making use Markov Chain Monte Carlo (MCMC) Integrated Nested Laplace Approximation (INLA) algorithms context estimation methods.