Analysis of Spatial Data with Chi-Square Copula

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.