Copula-based multiple indicator kriging for non-Gaussian random fields

Gaurav Agarwal, Ying Sun, Huixia J. Wang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


In 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.
Original languageEnglish (US)
Pages (from-to)100524
JournalSpatial Statistics
StatePublished - Jun 9 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-06-11
Acknowledged KAUST grant number(s): OSR-2019-CRG7-3800
Acknowledgements: The 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

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Statistics and Probability
  • Management, Monitoring, Policy and Law


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