Factor copula models for data with spatio-temporal dependence

Handle URI:
http://hdl.handle.net/10754/625889
Title:
Factor copula models for data with spatio-temporal dependence
Authors:
Krupskii, Pavel; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
We propose a new copula model for spatial data that are observed repeatedly in time. The model is based on the assumption that there exists a common factor that affects the measurements of a process in space and in time. Unlike models based on multivariate normality, our model can handle data with tail dependence and asymmetry. The likelihood for the proposed model can be obtained in a simple form and therefore parameter estimation is quite fast. Simulation from this model is straightforward and data can be predicted at any spatial location and time point. We use simulation studies to show different types of dependencies, both in space and in time, that can be generated by this model. We apply the proposed copula model to hourly wind data and compare its performance with some classical models for spatio-temporal data.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Krupskii P, Genton MG (2017) Factor copula models for data with spatio-temporal dependence. Spatial Statistics. Available: http://dx.doi.org/10.1016/j.spasta.2017.10.001.
Publisher:
Elsevier BV
Journal:
Spatial Statistics
Issue Date:
13-Oct-2017
DOI:
10.1016/j.spasta.2017.10.001
Type:
Article
ISSN:
2211-6753
Sponsors:
This research was supported by the King Abdullah University of Science and Technology (KAUST) .
Additional Links:
http://www.sciencedirect.com/science/article/pii/S2211675317300210
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.authorGenton, Marc G.en
dc.date.accessioned2017-10-17T11:47:40Z-
dc.date.available2017-10-17T11:47:40Z-
dc.date.issued2017-10-13en
dc.identifier.citationKrupskii P, Genton MG (2017) Factor copula models for data with spatio-temporal dependence. Spatial Statistics. Available: http://dx.doi.org/10.1016/j.spasta.2017.10.001.en
dc.identifier.issn2211-6753en
dc.identifier.doi10.1016/j.spasta.2017.10.001en
dc.identifier.urihttp://hdl.handle.net/10754/625889-
dc.description.abstractWe propose a new copula model for spatial data that are observed repeatedly in time. The model is based on the assumption that there exists a common factor that affects the measurements of a process in space and in time. Unlike models based on multivariate normality, our model can handle data with tail dependence and asymmetry. The likelihood for the proposed model can be obtained in a simple form and therefore parameter estimation is quite fast. Simulation from this model is straightforward and data can be predicted at any spatial location and time point. We use simulation studies to show different types of dependencies, both in space and in time, that can be generated by this model. We apply the proposed copula model to hourly wind data and compare its performance with some classical models for spatio-temporal data.en
dc.description.sponsorshipThis research was supported by the King Abdullah University of Science and Technology (KAUST) .en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S2211675317300210en
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, 10 October 2017. DOI: 10.1016/j.spasta.2017.10.001. © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectCopulaen
dc.subjectHeavy tailsen
dc.subjectNon-Gaussian random fielden
dc.subjectSpatial statisticsen
dc.subjectTail asymmetryen
dc.subjectTemporal dependenceen
dc.titleFactor copula models for data with spatio-temporal dependenceen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalSpatial Statisticsen
dc.eprint.versionPost-printen
kaust.authorKrupskii, Pavelen
kaust.authorGenton, Marc G.en
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