GEOSTATISTICAL BINARY DATA: MODELS, PROPERTIES AND CONNECTIONS

Victor De Oliveira ()
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Victor De Oliveira: UTSA

Working Papers from College of Business, University of Texas at San Antonio

Abstract: This work considers models for geostatistical data for situations in which the region where the phenomenon of interest varies is partitioned into two disjoint subregions, which is 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 a spatial multivariate probit model that eases regression function modeling, interpretation of the regression parameters, and establishing connections with other models. The second-order properties of this model are studied in some detail. Finally, connections between the aforementioned classes of models are established, showing that some of these are reformulations (reparametrizations) of the other models.

Keywords: Clipped Gaussian random field; Gaussian copula model; Generalized linear mixed model; Indicator kriging; Multivariate probit model. (search for similar items in EconPapers)
JEL-codes: C21 C31 C53 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2017-12-10
New Economics Papers: this item is included in nep-ecm
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Published in Review of Economics, March 1999, pages 1-23

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Persistent link: https://EconPapers.repec.org/RePEc:tsa:wpaper:0151mss

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