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dc.contributor.authorKrupskii, Pavel
dc.contributor.authorHuser, Raphaël
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2017-02-28T12:11:05Z
dc.date.available2017-02-28T12:11:05Z
dc.date.issued2016-12-16
dc.identifier.citationKrupskii P, Huser R, Genton MG (2016) Factor Copula Models for Replicated Spatial Data. Journal of the American Statistical Association: 0–0. Available: http://dx.doi.org/10.1080/01621459.2016.1261712.
dc.identifier.issn0162-1459
dc.identifier.issn1537-274X
dc.identifier.doi10.1080/01621459.2016.1261712
dc.identifier.urihttp://hdl.handle.net/10754/622944
dc.description.abstractWe propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.
dc.description.sponsorshipThis research was supported by the King Abdullah University of Science and Technology (KAUST).
dc.publisherInforma UK Limited
dc.relation.urlhttp://www.tandfonline.com/doi/full/10.1080/01621459.2016.1261712
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on 16 Dec 2016, available online: http://wwww.tandfonline.com/10.1080/01621459.2016.1261712.
dc.subjectcopula
dc.subjectheavy tails
dc.subjectnon-Gaussian random field
dc.subjectspatial statistics
dc.subjecttail asymmetry
dc.titleFactor Copula Models for Replicated Spatial Data
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalJournal of the American Statistical Association
dc.eprint.versionPost-print
dc.identifier.arxivid1511.03000
kaust.personKrupskiy, Pavel
kaust.personHuser, Raphaël
kaust.personGenton, Marc G.
dc.relation.issupplementedbyDOI:10.6084/m9.figshare.4478411
refterms.dateFOA2018-06-16T00:00:00Z
display.relations<b> Is Supplemented By:</b> <br/> <ul><li><i>[Dataset]</i> <br/> Krupskii, P., Huser, R., & Genton, M. G. (2016). Factor Copula Models for Replicated Spatial Data. Figshare. https://doi.org/10.6084/m9.figshare.4478411. DOI: <a href="https://doi.org/10.6084/m9.figshare.4478411">10.6084/m9.figshare.4478411</a> HANDLE: <a href="http://hdl.handle.net/10754/624778">10754/624778</a></li></ul>
dc.date.published-online2016-12-16
dc.date.published-print2018-01-02


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