An M-estimator of spatial tail dependence

John H.J. Einmahl, Anna Kiriliouk, Andrea Krajina, Johan Segers

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Résumé

Tail dependence models for distributions attracted to a max-stable law are fitted by using observations above a high threshold. To cope with spatial, high dimensional data, a rank-based M-estimator is proposed relying on bivariate margins only. A data-driven weight matrix is used to minimize the asymptotic variance. Empirical process arguments show that the estimator is consistent and asymptotically normal. Its finite sample performance is assessed in simulation experiments involving popular max-stable processes perturbed with additive noise. An analysis of wind speed data from the Netherlands illustrates the method.

langue originaleAnglais
Pages (de - à)275-298
Nombre de pages24
journalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume78
Numéro de publication1
Les DOIs
Etat de la publicationPublié - 1 janv. 2016
Modification externeOui

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