Resumen de Bayesian Spatial Conditional Overdispersion Models: Application to infant mortality

Mabel Morales Otero, Vicente A. Núñez Antón

• In this work we revise Bayesian generalized conditional models for spatial count data with overdispersion. We show their usefulness by tting them to infant mortality rates from Colombian regions. These models assume that the overdispersion present in the data may be caused partially from the spatial dependence that exists among the spatial units. Therefore, regression structures are specied both for the conditional mean and for the dispersion parameter, including also spatial neighborhood structures in the model. We work on the case of spatial count data which follow a Poisson distribution, and focus our attention on the spatial generalized conditional normal Poisson model. Models have been tted with the use of the Markov Chain Monte Carlo (MCMC) algorithms within the context of Bayesian estimation methods.