Copula-based multiple indicator kriging for non-Gaussian random fields
KAUST DepartmentStatistics Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2019-CRG7-3800
Embargo End Date2023-06-09
Permanent link to this recordhttp://hdl.handle.net/10754/669502
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AbstractIn spatial statistics, the kriging predictor is the best linear predictor at unsampled locations, but not the optimal predictor for non-Gaussian processes. In this paper, we introduce a copula-based multiple indicator kriging model for the analysis of non-Gaussian spatial data by thresholding the spatial observations at a given set of quantile values. The proposed copula model allows for flexible marginal distributions while modeling the spatial dependence via copulas. We show that the covariances required by kriging have a direct link to the chosen copula function. We then develop a semiparametric estimation procedure. The proposed method provides the entire predictive distribution function at a new location, and thus allows for both point and interval predictions. The proposed method demonstrates better predictive performance than the commonly used variogram approach and Gaussian kriging in the simulation studies. We illustrate our methods on precipitation data in Spain during November 2019, and heavy metal dataset in topsoil along the river Meuse, and obtain probability exceedance maps.
CitationAgarwal, G., Sun, Y., & Wang, H. J. (2021). Copula-based multiple indicator kriging for non-Gaussian random fields. Spatial Statistics, 100524. doi:10.1016/j.spasta.2021.100524
SponsorsThe research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) under award number OSR-2019-CRG7-3800. The precipitation dataset used in this research was taken from the European Climate Assessment & Dataset (ECA&D) project available at https://www.ecad.eu.