Factor Copula Models for Replicated Spatial Data

Handle URI:
http://hdl.handle.net/10754/622944
Title:
Factor Copula Models for Replicated Spatial Data
Authors:
Krupskii, Pavel; Huser, Raphaël ( 0000-0002-1228-2071 ) ; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
We 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Krupskii 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.
Publisher:
Informa UK Limited
Journal:
Journal of the American Statistical Association
Issue Date:
19-Dec-2016
DOI:
10.1080/01621459.2016.1261712
Type:
Article
ISSN:
0162-1459; 1537-274X
Sponsors:
This research was supported by the King Abdullah University of Science and Technology (KAUST).
Is Supplemented By:
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:10.6084/m9.figshare.4478411; HANDLE:http://hdl.handle.net/10754/624778
Additional Links:
http://www.tandfonline.com/doi/full/10.1080/01621459.2016.1261712
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorKrupskii, Pavelen
dc.contributor.authorHuser, Raphaëlen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2017-02-28T12:11:05Z-
dc.date.available2017-02-28T12:11:05Z-
dc.date.issued2016-12-19en
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.en
dc.identifier.issn0162-1459en
dc.identifier.issn1537-274Xen
dc.identifier.doi10.1080/01621459.2016.1261712en
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.en
dc.description.sponsorshipThis research was supported by the King Abdullah University of Science and Technology (KAUST).en
dc.publisherInforma UK Limiteden
dc.relation.urlhttp://www.tandfonline.com/doi/full/10.1080/01621459.2016.1261712en
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.en
dc.subjectcopulaen
dc.subjectheavy tailsen
dc.subjectnon-Gaussian random fielden
dc.subjectspatial statisticsen
dc.subjecttail asymmetryen
dc.titleFactor Copula Models for Replicated Spatial Dataen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalJournal of the American Statistical Associationen
dc.eprint.versionPost-printen
kaust.authorKrupskii, Pavelen
kaust.authorHuser, Raphaëlen
kaust.authorGenton, Marc G.en
dc.relation.isSupplementedByKrupskii, P., Huser, R., & Genton, M. G. (2016). Factor Copula Models for Replicated Spatial Data. Figshare. https://doi.org/10.6084/m9.figshare.4478411en
dc.relation.isSupplementedByDOI:10.6084/m9.figshare.4478411en
dc.relation.isSupplementedByHANDLE:http://hdl.handle.net/10754/624778en
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