Semi-parametric pairwise inference methods in spatial models based on copulas

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• 作者：Jean-Franç ; ois Quessy^a ; ^{jean-francois.quessy@uqtr.ca" class="auth_mail" title="E-mail the corresponding author} ; Louis-Paul Rivest^b ; ^{louis-paul.rivest@mat.ulaval.ca" class="auth_mail" title="E-mail the corresponding author} ; Marie-Hé ; lè ; ne Toupin^b ; ^{marie-helene.toupin.1@ulaval.ca" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Copula ; Pairwise likelihood ; Rank-based methods ; Spatial dependence ; Spatial interpolation
• 刊名：Spatial Statistics
• 出版年：2015
• 出版时间：November 2015
• 年：2015
• 卷：14
• 期：part_PC
• 页码：472-490
• 全文大小：1822 K

文摘

In this paper, semi-parametric models based on copulas are considered for the modeling of stationary and isotropic spatial random fields. To this end, a general family of multivariate distributions is introduced in which the dependence structure between any finite sets of locations is modeled via a copula and where the strength of the relationships between any two locations is controlled by a link function. Because the density of most of the multivariate spatial copulas is untractable, it is proposed that inferential procedures for these models be based on a pairwise approach taking into account the bivariate densities only. Specifically, a rank-based estimation procedure using the so-called pairwise likelihood is proposed and a semi-parametric spatial interpolation method for the prediction at un-sampled locations is developed; both methods are investigated with the help of simulated spatial random fields. The usefulness of the newly introduced tools is illustrated on the Meuse river data.