Likelihood estimators for multivariate extremes

Raphaël Huser, Anthony C. Davison, Marc G. Genton

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


The 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.
Original languageEnglish (US)
Pages (from-to)79-103
Number of pages25
Issue number1
StatePublished - Nov 17 2015

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KAUST Repository Item: Exported on 2020-10-01


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