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dc.contributor.authorCastro Camilo, Daniela
dc.contributor.authorde Carvalho, Miguel
dc.date.accessioned2016-11-03T08:28:49Z
dc.date.available2016-11-03T08:28:49Z
dc.date.issued2016-05-11
dc.identifier.citationCastro Camilo D, de Carvalho M (2016) Spectral density regression for bivariate extremes. Stoch Environ Res Risk Assess. Available: http://dx.doi.org/10.1007/s00477-016-1257-z.
dc.identifier.issn1436-3240
dc.identifier.issn1436-3259
dc.identifier.doi10.1007/s00477-016-1257-z
dc.identifier.urihttp://hdl.handle.net/10754/621417
dc.description.abstractWe introduce a density regression model for the spectral density of a bivariate extreme value distribution, that allows us to assess how extremal dependence can change over a covariate. Inference is performed through a double kernel estimator, which can be seen as an extension of the Nadaraya–Watson estimator where the usual scalar responses are replaced by mean constrained densities on the unit interval. Numerical experiments with the methods illustrate their resilience in a variety of contexts of practical interest. An extreme temperature dataset is used to illustrate our methods. © 2016 Springer-Verlag Berlin Heidelberg
dc.description.sponsorshipFondecyt
dc.publisherSpringer Nature
dc.subjectBivariate extremes values
dc.subjectNonstationary extremal dependence structures
dc.subjectSpectral density
dc.subjectStatistics of extremes
dc.titleSpectral density regression for bivariate extremes
dc.typeArticle
dc.contributor.departmentThuwal, Saudi Arabia
dc.identifier.journalStochastic Environmental Research and Risk Assessment
dc.contributor.institutionPontificia Universidad Católica de Chile, Santiago, Chile
kaust.personCastro Camilo, Daniela
dc.date.published-online2016-05-11
dc.date.published-print2017-09


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