Considering groups in the statistical modeling of spatio-temporal data
AbstractSpatio-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 sources 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 locations can be reflected in a hierarchical model and assessed the performances of different models via a simulation study.
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