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dc.contributor.authorKrupskiy, Pavel
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2018-09-30T12:13:03Z
dc.date.available2018-09-30T12:13:03Z
dc.date.issued2018-09-24
dc.identifier.citationKrupskii P, Genton MG (2018) A copula model for non-Gaussian multivariate spatial data. Journal of Multivariate Analysis. Available: http://dx.doi.org/10.1016/j.jmva.2018.09.007.
dc.identifier.issn0047-259X
dc.identifier.doi10.1016/j.jmva.2018.09.007
dc.identifier.urihttp://hdl.handle.net/10754/628852
dc.description.abstractWe propose a new copula model for replicated multivariate spatial data. Unlike classical models that assume multivariate normality of the data, the proposed copula is based on the assumption that some factors exist that affect the joint spatial dependence of all measurements of each variable as well as the joint dependence among these variables. The model is parameterized in terms of a cross-covariance function that may be chosen from the many models proposed in the literature. In addition, there are additive factors in the model that allow tail dependence and reflection asymmetry of each variable measured at different locations, and of different variables to be modeled. The proposed approach can therefore be seen as an extension of the linear model of coregionalization widely used for modeling multivariate spatial data. The likelihood of the model can be obtained in a simple form and, therefore, the likelihood estimation is quite fast. The model is not restricted to the set of data locations, and using the estimated copula, spatial data can be interpolated at locations where values of variables are unknown. We apply the proposed model to temperature and pressure data, and we compare its performance with that of a popular model from multivariate geostatistics.
dc.description.sponsorshipThis research was supported by the King Abdullah University of Science and Technology (KAUST) . The authors would like to thank the associate editor and external referee for their constructive comments that led to an improved presentation.
dc.publisherElsevier BV
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S0047259X18301696
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Journal of Multivariate Analysis. 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 Journal of Multivariate Analysis, [, , (2018-09-24)] DOI: 10.1016/j.jmva.2018.09.007 . © 2018. 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.subjectCopula
dc.subjectHeavy tails
dc.subjectPermutation asymmetry
dc.subjectSpatial statistics
dc.subjectTail asymmetry
dc.titleA copula model for non-Gaussian multivariate spatial data
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalJournal of Multivariate Analysis
dc.eprint.versionPost-print
dc.identifier.arxivid1603.03950
kaust.personKrupskiy, Pavel
kaust.personGenton, Marc G.
refterms.dateFOA2018-10-01T08:13:35Z
dc.date.published-online2018-09-24
dc.date.published-print2019-01


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