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dc.contributor.authorVettori, Sabrina
dc.contributor.authorHuser, Raphaël
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2017-05-18T08:39:57Z
dc.date.available2017-05-18T08:39:57Z
dc.date.issued2017-05-11
dc.identifier.citationVettori S, Huser R, Genton MG (2017) A comparison of dependence function estimators in multivariate extremes. Statistics and Computing. Available: http://dx.doi.org/10.1007/s11222-017-9745-7.
dc.identifier.issn0960-3174
dc.identifier.issn1573-1375
dc.identifier.doi10.1007/s11222-017-9745-7
dc.identifier.urihttp://hdl.handle.net/10754/623662
dc.description.abstractVarious nonparametric and parametric estimators of extremal dependence have been proposed in the literature. Nonparametric methods commonly suffer from the curse of dimensionality and have been mostly implemented in extreme-value studies up to three dimensions, whereas parametric models can tackle higher-dimensional settings. In this paper, we assess, through a vast and systematic simulation study, the performance of classical and recently proposed estimators in multivariate settings. In particular, we first investigate the performance of nonparametric methods and then compare them with classical parametric approaches under symmetric and asymmetric dependence structures within the commonly used logistic family. We also explore two different ways to make nonparametric estimators satisfy the necessary dependence function shape constraints, finding a general improvement in estimator performance either (i) by substituting the estimator with its greatest convex minorant, developing a computational tool to implement this method for dimensions $$D\ge 2$$D≥2 or (ii) by projecting the estimator onto a subspace of dependence functions satisfying such constraints and taking advantage of Bernstein–Bézier polynomials. Implementing the convex minorant method leads to better estimator performance as the dimensionality increases.
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/article/10.1007/s11222-017-9745-7
dc.relation.urlhttps://stsda.kaust.edu.sa/Documents/2017.VHG.SC.final.pdf
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s11222-017-9745-7
dc.subjectAsymmetric logistic model
dc.subjectComponentwise maxima
dc.subjectConvexity
dc.subjectCopula
dc.subjectGreatest convex minorant
dc.subjectNonparametric and parametric estimators
dc.subjectPickands dependence function
dc.titleA comparison of dependence function estimators in multivariate extremes
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEntrepreneurship Center
dc.contributor.departmentStatistics Program
dc.identifier.journalStatistics and Computing
dc.eprint.versionPost-print
kaust.personVettori, Sabrina
kaust.personHuser, Raphaël
kaust.personGenton, Marc G.
refterms.dateFOA2018-05-11T00:00:00Z
dc.date.published-online2017-05-11
dc.date.published-print2018-05


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