Non-linear models for extremal dependence

Détails

ID Serval
serval:BIB_B206AA72272C
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Non-linear models for extremal dependence
Périodique
Journal of Multivariate Analysis
Auteur(s)
Mhalla L., Chavez-Demoulin V., Naveau P.
ISSN
0047-259X
Statut éditorial
Publié
Date de publication
07/2017
Peer-reviewed
Oui
Volume
159
Pages
49-66
Langue
anglais
Résumé
The dependence structure of max-stable random vectors can be characterized by their Pickands dependence function. In many applications, the extremal dependence measure varies with covariates. We develop a flexible, semi-parametric method for the estimation of non-stationary multivariate Pickands dependence functions. The proposed construction is based on an accurate max-projection allowing to pass from the multivariate to the univariate setting and to rely on the generalized additive modeling framework. In the bivariate case, the resulting estimator of the Pickands function is regularized using constrained median smoothing B-splines, and bootstrap variability bands are constructed. In higher dimensions, we tailor our approach to the estimation of the extremal coefficient. An extended simulation study suggests that our estimator performs well and is competitive with the standard estimators in the absence of covariates. We apply the new methodology to a temperature dataset in the US where the extremal dependence is linked to time and altitude.
Mots-clé
Extreme value theory, Generalized additive models, Max-stable random vectors, Non-stationarity, Pickands function, Semi-parametric models, Temperature data, Statistics, Probability and Uncertainty, Statistics and Probability, Numerical Analysis
Web of science
Création de la notice
03/04/2017 10:19
Dernière modification de la notice
20/08/2019 15:20
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