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dc.contributor.authorHuser, Raphaël
dc.contributor.authorDavison, Anthony C.
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2015-12-16T13:07:23Z
dc.date.available2015-12-16T13:07:23Z
dc.date.issued2015-11-17
dc.identifier.citationLikelihood estimators for multivariate extremes 2015 Extremes
dc.identifier.issn1386-1999
dc.identifier.issn1572-915X
dc.identifier.doi10.1007/s10687-015-0230-4
dc.identifier.urihttp://hdl.handle.net/10754/583987
dc.description.abstractThe main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.
dc.language.isoen
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/10.1007/s10687-015-0230-4
dc.rightsArchived with thanks to Extremes. The final publication is available at Springer via http://dx.doi.org/10.1007/s10687-015-0230-4.
dc.subjectAsymptotic relative efficiency
dc.subjectCensored likelihood
dc.subjectLogistic model
dc.subjectMultivariate extremes
dc.subjectPairwise likelihood
dc.subjectPoint process approach
dc.titleLikelihood estimators for multivariate extremes
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalExtremes
dc.eprint.versionPost-print
dc.contributor.institutionEPFL, FSB-MATHAA-STAT, Station 8, Bâtiment MA, 1015, Lausanne, Switzerland
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
dc.identifier.arxivid1411.3448
kaust.personHuser, Raphaël
kaust.personGenton, Marc G.
refterms.dateFOA2016-11-17T00:00:00Z
dc.date.published-online2015-11-17
dc.date.published-print2016-03


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