Non-Stationary Dependence Structures for Spatial Extremes

Handle URI:
http://hdl.handle.net/10754/611774
Title:
Non-Stationary Dependence Structures for Spatial Extremes
Authors:
Huser, Raphaël ( 0000-0002-1228-2071 ) ; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
Max-stable processes are natural models for spatial extremes because they provide suitable asymptotic approximations to the distribution of maxima of random fields. In the recent past, several parametric families of stationary max-stable models have been developed, and fitted to various types of data. However, a recurrent problem is the modeling of non-stationarity. In this paper, we develop non-stationary max-stable dependence structures in which covariates can be easily incorporated. Inference is performed using pairwise likelihoods, and its performance is assessed by an extensive simulation study based on a non-stationary locally isotropic extremal t model. Evidence that unknown parameters are well estimated is provided, and estimation of spatial return level curves is discussed. The methodology is demonstrated with temperature maxima recorded over a complex topography. Models are shown to satisfactorily capture extremal dependence.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Non-Stationary Dependence Structures for Spatial Extremes 2016 Journal of Agricultural, Biological, and Environmental Statistics
Publisher:
Springer Nature
Journal:
Journal of Agricultural, Biological, and Environmental Statistics
Issue Date:
3-Mar-2016
DOI:
10.1007/s13253-016-0247-4
Type:
Article
ISSN:
1085-7117; 1537-2693
Additional Links:
http://link.springer.com/10.1007/s13253-016-0247-4
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHuser, Raphaëlen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2016-06-05T14:29:01Z-
dc.date.available2016-06-05T14:29:01Z-
dc.date.issued2016-03-03-
dc.identifier.citationNon-Stationary Dependence Structures for Spatial Extremes 2016 Journal of Agricultural, Biological, and Environmental Statisticsen
dc.identifier.issn1085-7117-
dc.identifier.issn1537-2693-
dc.identifier.doi10.1007/s13253-016-0247-4-
dc.identifier.urihttp://hdl.handle.net/10754/611774-
dc.description.abstractMax-stable processes are natural models for spatial extremes because they provide suitable asymptotic approximations to the distribution of maxima of random fields. In the recent past, several parametric families of stationary max-stable models have been developed, and fitted to various types of data. However, a recurrent problem is the modeling of non-stationarity. In this paper, we develop non-stationary max-stable dependence structures in which covariates can be easily incorporated. Inference is performed using pairwise likelihoods, and its performance is assessed by an extensive simulation study based on a non-stationary locally isotropic extremal t model. Evidence that unknown parameters are well estimated is provided, and estimation of spatial return level curves is discussed. The methodology is demonstrated with temperature maxima recorded over a complex topography. Models are shown to satisfactorily capture extremal dependence.en
dc.language.isoenen
dc.publisherSpringer Natureen
dc.relation.urlhttp://link.springer.com/10.1007/s13253-016-0247-4en
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s13253-016-0247-4en
dc.subjectCovariateen
dc.subjectExtremal t modelen
dc.subjectExtreme eventen
dc.subjectMax-stable processen
dc.subjectNon-stationarityen
dc.titleNon-Stationary Dependence Structures for Spatial Extremesen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalJournal of Agricultural, Biological, and Environmental Statisticsen
dc.eprint.versionPost-printen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorHuser, Raphaëlen
kaust.authorGenton, Marc G.en
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