Supplementary Material for: Factor Copula Models for Replicated Spatial Data

Handle URI:
http://hdl.handle.net/10754/624778
Title:
Supplementary Material for: Factor Copula Models for Replicated Spatial Data
Authors:
Krupskii, Pavel; Huser, Raphaël ( 0000-0002-1228-2071 ) ; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
<p>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.</p>
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Krupskii, P., Huser, R., & Genton, M. G. (2016). Factor Copula Models for Replicated Spatial Data. Figshare. https://doi.org/10.6084/m9.figshare.4478411
Publisher:
Figshare
Issue Date:
2016
DOI:
10.6084/m9.figshare.4478411
Type:
Dataset
Is Supplement To:
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.; DOI:10.1080/01621459.2016.1261712; HANDLE:http://hdl.handle.net/10754/622944
Appears in Collections:
Datasets; 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.accessioned2017-06-06T08:33:44Z-
dc.date.available2017-06-06T08:33:44Z-
dc.date.created2016-12-16en
dc.date.issued2016en
dc.identifier.citationKrupskii, P., Huser, R., & Genton, M. G. (2016). Factor Copula Models for Replicated Spatial Data. Figshare. https://doi.org/10.6084/m9.figshare.4478411en
dc.identifier.doi10.6084/m9.figshare.4478411en
dc.identifier.urihttp://hdl.handle.net/10754/624778-
dc.description.abstract<p>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.</p>en
dc.format.extent78965 Bytesen
dc.publisherFigshareen
dc.rightsCC BYen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectEcologyen
dc.subjectCanceren
dc.subjectScience Policyen
dc.titleSupplementary Material for: Factor Copula Models for Replicated Spatial Dataen
dc.typeDataseten
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
kaust.authorHuser, Raphaëlen
kaust.authorGenton, Marc G.en
dc.type.resourcePaperen
dc.relation.isSupplementToKrupskii 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.relation.isSupplementToDOI:10.1080/01621459.2016.1261712en
dc.relation.isSupplementToHANDLE:http://hdl.handle.net/10754/622944en
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