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Abstract:
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This work consider models for geostatistical data for situations in which the region where the phenomenon of interest varies, say D, is partitioned into two disjoint subregions, D = B + W, called a binary map. The goals of this work are threefold. First, a review is provided of the classes of models that have been proposed so far for geostatistical binary data as well as a description of their main features. Second, a generalization is provided of the spatial multivariate probit model that eases regression function modeling, interpretation of the regression parameters, and establishing connections with other models. Finally, connections between the aforementioned classes of models are established, showing some of these are reformulations (reparametrizations) of the same model.
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