Extreme-value limit of the convolution of exponential and multivariate normal distributions: Link to the Hüsler–Reiß distribution

Handle URI:
http://hdl.handle.net/10754/626114
Title:
Extreme-value limit of the convolution of exponential and multivariate normal distributions: Link to the Hüsler–Reiß distribution
Authors:
Krupskii, Pavel; Joe, Harry; Lee, David; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
The multivariate Hüsler–Reiß copula is obtained as a direct extreme-value limit from the convolution of a multivariate normal random vector and an exponential random variable multiplied by a vector of constants. It is shown how the set of Hüsler–Reiß parameters can be mapped to the parameters of this convolution model. Assuming there are no singular components in the Hüsler–Reiß copula, the convolution model leads to exact and approximate simulation methods. An application of simulation is to check if the Hüsler–Reiß copula with different parsimonious dependence structures provides adequate fit to some data consisting of multivariate extremes.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Krupskii P, Joe H, Lee D, Genton MG (2017) Extreme-value limit of the convolution of exponential and multivariate normal distributions: Link to the Hüsler–Reiß distribution. Journal of Multivariate Analysis. Available: http://dx.doi.org/10.1016/j.jmva.2017.10.006.
Publisher:
Elsevier BV
Journal:
Journal of Multivariate Analysis
Issue Date:
2-Nov-2017
DOI:
10.1016/j.jmva.2017.10.006
Type:
Article
ISSN:
0047-259X
Sponsors:
This research was supported by the King Abdullah University of Science and Technology (KAUST), Discovery Grant No. 8698 from the Natural Sciences and Engineering Research Council of Canada, a Collaborative Research Team grant for the project Copula Dependence Modeling: Theory and Applications of the Canadian Statistical Sciences Institute (CANSSI), and a University of British Columbia four-year doctoral fellowship. We are grateful to the two referees, the Associate Editor, and the Editor-in-Chief for their comments.
Additional Links:
http://www.sciencedirect.com/science/article/pii/S0047259X17301525
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorKrupskii, Pavelen
dc.contributor.authorJoe, Harryen
dc.contributor.authorLee, Daviden
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2017-11-06T07:09:04Z-
dc.date.available2017-11-06T07:09:04Z-
dc.date.issued2017-11-02en
dc.identifier.citationKrupskii P, Joe H, Lee D, Genton MG (2017) Extreme-value limit of the convolution of exponential and multivariate normal distributions: Link to the Hüsler–Reiß distribution. Journal of Multivariate Analysis. Available: http://dx.doi.org/10.1016/j.jmva.2017.10.006.en
dc.identifier.issn0047-259Xen
dc.identifier.doi10.1016/j.jmva.2017.10.006en
dc.identifier.urihttp://hdl.handle.net/10754/626114-
dc.description.abstractThe multivariate Hüsler–Reiß copula is obtained as a direct extreme-value limit from the convolution of a multivariate normal random vector and an exponential random variable multiplied by a vector of constants. It is shown how the set of Hüsler–Reiß parameters can be mapped to the parameters of this convolution model. Assuming there are no singular components in the Hüsler–Reiß copula, the convolution model leads to exact and approximate simulation methods. An application of simulation is to check if the Hüsler–Reiß copula with different parsimonious dependence structures provides adequate fit to some data consisting of multivariate extremes.en
dc.description.sponsorshipThis research was supported by the King Abdullah University of Science and Technology (KAUST), Discovery Grant No. 8698 from the Natural Sciences and Engineering Research Council of Canada, a Collaborative Research Team grant for the project Copula Dependence Modeling: Theory and Applications of the Canadian Statistical Sciences Institute (CANSSI), and a University of British Columbia four-year doctoral fellowship. We are grateful to the two referees, the Associate Editor, and the Editor-in-Chief for their comments.en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0047259X17301525en
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Journal of Multivariate Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Multivariate Analysis, [, , (2017-11-02)] DOI: 10.1016/j.jmva.2017.10.006 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectCopulaen
dc.subjectExtreme-value limiten
dc.subjectParametric bootstrapen
dc.subjectParsimonious dependenceen
dc.titleExtreme-value limit of the convolution of exponential and multivariate normal distributions: Link to the Hüsler–Reiß distributionen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalJournal of Multivariate Analysisen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartment of Statistics, University of British Columbia, 2207 Main Mall, Vancouver, BC, Canada V6T 1Z4en
kaust.authorKrupskii, Pavelen
kaust.authorGenton, Marc G.en
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