Models for Geostatistical Binary Data: Properties and Connections.
This article explores models for geostatistical data for situations in which the region where the phenomenon of interest varies is partitioned into two disjoint subregions. This is called a binary map. The goals of the article are 3-fold. First, a review is provided of the classes of models that hav...
| Published in: | American Statistician Vol. 74; no. 1; pp. 72 - 80 |
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| Format: | Article |
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Taylor & Francis Ltd
Feb2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=141377216&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 141377216 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: Feb2020 vid: 74 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 141377216 10.1080/00031305.2018.1444674 ppf: 72 ppct: 8 formats: tig: atl: Models for Geostatistical Binary Data: Properties and Connections. aug: au: De Oliveira, Victor affil: Department of Management Science and Statistics, The University of Texas at San Antonio, San Antonio, TX su: Random fields Gaussian processes Regression analysis Binary codes sug: subj: Random fields Gaussian processes Regression analysis Binary codes keyword: Clipped Gaussian random field Gaussian copula model Generalized linear mixed model Indicator kriging Probit model Clipped Gaussian random field Gaussian copula model Generalized linear mixed model Indicator kriging Probit model ab: This article explores models for geostatistical data for situations in which the region where the phenomenon of interest varies is partitioned into two disjoint subregions. This is called a binary map. The goals of the article are 3-fold. First, a review is provided of the classes of models that have been proposed so far in the literature for geostatistical binary data as well as a description of their main features. A problems with the use of moment-based models is pointed out. Second, a generalization is provided of the clipped Gaussian random field 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 (reparameterizations) of the other classes of models. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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