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dc.contributor.authorAgarwal, Gaurav
dc.contributor.authorSun, Ying
dc.contributor.authorWang, Huixia J.
dc.date.accessioned2021-06-10T08:03:26Z
dc.date.available2021-06-10T08:03:26Z
dc.date.issued2021-06-09
dc.date.submitted2020-12-26
dc.identifier.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
dc.identifier.issn2211-6753
dc.identifier.doi10.1016/j.spasta.2021.100524
dc.identifier.urihttp://hdl.handle.net/10754/669502
dc.description.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.
dc.description.sponsorshipThe 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.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S2211675321000348
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, [, , (2021-06-09)] DOI: 10.1016/j.spasta.2021.100524 . © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleCopula-based multiple indicator kriging for non-Gaussian random fields
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentEnvironmental Statistics Group
dc.contributor.departmentStatistics Program
dc.identifier.journalSpatial Statistics
dc.rights.embargodate2023-06-09
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Statistics, George Washington University, Washington, D.C, United States of America.
dc.identifier.pages100524
kaust.personAgarwal, Gaurav
kaust.personSun, Ying
kaust.grant.numberOSR-2019-CRG7-3800
dc.date.accepted2021-06-01
refterms.dateFOA2021-06-10T08:04:26Z
kaust.acknowledged.supportUnitCRG
kaust.acknowledged.supportUnitOSR
dc.date.published-online2021-06-09
dc.date.published-print2021-06


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