Considering group in the satistical modelig of spatio-temporal data

Spatio-temporal statistical methods are developing into an important research topic that goes beyond the study of processes that generate independent, identically distributed observations. Hierarchical models are a suitable proposal for both continuous and discrete spatio-temporal domains. They are flexible and permit separation of the various source of uncertainty by means of a sequence of conditional models. In this work, we expanded on spatio-temporal data modeling by considering data categorization with respect to certain differentiating features. We studied the impact of the presence of subgroups on model building with emphasis on Bayesian modeling. We discussed how differences in spatial location can be reflected in e a hierarchical model and assessed the performances models via a simulation study.