Spectral density regression for bivariate extremes

Handle URI:
http://hdl.handle.net/10754/621417
Title:
Spectral density regression for bivariate extremes
Authors:
Castro Camilo, Daniela; de Carvalho, Miguel ( 0000-0003-3248-6984 )
Abstract:
We 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
KAUST Department:
Thuwal, Saudi Arabia
Citation:
Castro 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.
Publisher:
Springer Science + Business Media
Journal:
Stochastic Environmental Research and Risk Assessment
Issue Date:
11-May-2016
DOI:
10.1007/s00477-016-1257-z
Type:
Article
ISSN:
1436-3240; 1436-3259
Sponsors:
Fondecyt
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorCastro Camilo, Danielaen
dc.contributor.authorde Carvalho, Miguelen
dc.date.accessioned2016-11-03T08:28:49Z-
dc.date.available2016-11-03T08:28:49Z-
dc.date.issued2016-05-11en
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.en
dc.identifier.issn1436-3240en
dc.identifier.issn1436-3259en
dc.identifier.doi10.1007/s00477-016-1257-zen
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 Heidelbergen
dc.description.sponsorshipFondecyten
dc.publisherSpringer Science + Business Mediaen
dc.subjectBivariate extremes valuesen
dc.subjectNonstationary extremal dependence structuresen
dc.subjectSpectral densityen
dc.subjectStatistics of extremesen
dc.titleSpectral density regression for bivariate extremesen
dc.typeArticleen
dc.contributor.departmentThuwal, Saudi Arabiaen
dc.identifier.journalStochastic Environmental Research and Risk Assessmenten
dc.contributor.institutionPontificia Universidad Católica de Chile, Santiago, Chileen
kaust.authorCastro Camilo, Danielaen
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