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Analysis of Spatial Data with Chi-Square Copula
Ronak Jamshidi , Sedigheh Shams
Abstract:   (1745 Views)
In this paper‎, ‎a family of copula functions called chi-square copula family is used for modeling the dependency structure of stationary and isotropic spatial random fields‎. ‎The dependence structure of this copula is such that‎, ‎it generalizes the Gaussian copula and flexible for modeling for high-dimensional random vectors and unlike Gaussian copula it allows for modeling of tail asymmetric dependence structures‎. ‎Since the density function of chi-square copula in high dimension has computational complexity‎, ‎therefore to estimate its parameters‎, ‎a composite pairwise likelihood method is used in which only bivariate density functions are used‎. ‎The purpose of this paper is to investigate the properties of the chi-square copula family‎, ‎estimating its parameters with the composite pairwise likelihood and its application in spatial interpolation.
Keywords: ‎Chi-Square Copula‎, ‎Composite Pairwise Likelihood‎, ‎Isotropic Spatial Random Field‎, ‎Spatial Interpolation.
Full-Text [PDF 3915 kb]   (242 Downloads)    
Type of Study: Research | Subject: Spatial Statistics
Received: 2018/04/9 | Accepted: 2018/12/15
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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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