Multivariate max-stable spatial processes

Handle URI:
http://hdl.handle.net/10754/552385
Title:
Multivariate max-stable spatial processes
Authors:
Genton, Marc G. ( 0000-0001-6467-2998 ) ; Padoan, S. A.; Sang, H.
Abstract:
Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown–Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and an application to daily maximum wind speed and wind gust.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Multivariate max-stable spatial processes 2015, 102 (1):215 Biometrika
Publisher:
Oxford University Press (OUP)
Journal:
Biometrika
Issue Date:
11-Feb-2015
DOI:
10.1093/biomet/asu066
Type:
Article
ISSN:
0006-3444; 1464-3510
Additional Links:
http://biomet.oxfordjournals.org/cgi/doi/10.1093/biomet/asu066
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorGenton, Marc G.en
dc.contributor.authorPadoan, S. A.en
dc.contributor.authorSang, H.en
dc.date.accessioned2015-05-06T13:29:06Zen
dc.date.available2015-05-06T13:29:06Zen
dc.date.issued2015-02-11en
dc.identifier.citationMultivariate max-stable spatial processes 2015, 102 (1):215 Biometrikaen
dc.identifier.issn0006-3444en
dc.identifier.issn1464-3510en
dc.identifier.doi10.1093/biomet/asu066en
dc.identifier.urihttp://hdl.handle.net/10754/552385en
dc.description.abstractMax-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown–Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and an application to daily maximum wind speed and wind gust.en
dc.publisherOxford University Press (OUP)en
dc.relation.urlhttp://biomet.oxfordjournals.org/cgi/doi/10.1093/biomet/asu066en
dc.rightsArchived with thanks to Biometrikaen
dc.subjectComposite likelihooden
dc.subjectCross-correlationen
dc.subjectExtremal coefficienten
dc.subjectMax-stable processen
dc.subjectMultivariate analysisen
dc.subjectRandom fielden
dc.subjectSpatial extremeen
dc.titleMultivariate max-stable spatial processesen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalBiometrikaen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartment of Decision Sciences, Bocconi University of Milan, 20136 Milano, Italyen
dc.contributor.institutionDepartment of Statistics, Texas A&M University, College Station, Texas 77843, U.S.A.en
kaust.authorGenton, Marc G.en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.