Geostatistical Modeling Using Non-gaussian Copulas

Hannes Kazianka1 and Jürgen Pilz2
1.Department of Business Mathematics, Vienna University of Technology, Vienna, Austria
2.Department of Statistics, Alpen-Adria University of Klagenfurt, Klagenfurt, Austria
Hannes.kazianka@tuwien.ac.at; juergen.pilz@uni-klu.ac.at

Abstract: Copula-based spatial models have recently attractedmuch attention and are used as a flexible tool for spatialinterpolation. For computational reasons, in most applicationsonly the radially symmetric Gaussian copula is employed.However, radial asymmetry is a property often observed inenvironmental data i.e. high values of the data have a strongerspatial dependence than low values. This paper presents a casestudy where radiological measurements have been taken in theregion of Gomel, near Tschernobyl. We show that copulamodels that are based on the radially asymmetric chi-squared-copula outperform the Gaussian copula models and classicalinterpolation methods like ordinary kriging.

Keywords: copula; spatial interpolation; radial asymmetry; Bayesian prediction; predictive distribution

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