An M-estimator for tail dependence in arbitrary dimensions

J.H.J. Einmahl, A. Krajina, J. Segers

Research output: Contribution to journalArticleScientificpeer-review

35 Citations (Scopus)
278 Downloads (Pure)

Abstract

Consider a random sample in the max-domain of attraction of a multivariate extreme value distribution such that the dependence structure of the attractor belongs to a parametric model. A new estimator for the unknown parameter is defined as the value that minimizes the distance between a vector of weighted integrals of the tail dependence function and their empirical counterparts. The minimization problem has, with probability tending to one, a unique, global solution. The estimator is consistent and asymptotically normal. The spectral measures of the tail dependence models to which the method applies can be discrete or continuous. Examples demonstrate the applicability and the performance of the method.
Original languageEnglish
Pages (from-to)1764-1793
JournalThe Annals of Statistics
Volume40
Issue number3
Publication statusPublished - 2012

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