Copulas are a flexible tool to model dependence of random variables. They cover the range from perfect negative to positive dependence, include the independent case and incorporate asymmetric dependence as well as the widely used Gaussian dependence structure. The pair-copula construction for multivariate copulas exploits the ease of bivariate copulas and suggests a decomposition of a multivariate copula into a set of bivariate ones. We successfully adapted this approach for spatial data and developed a powerful spatial pair-copula based interpolation method.
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Gräler et al. (2011) studied this question.
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