Nonparametric estimation of an extreme-value copula in arbitrary dimensions

DOI10.1016/j.jmva.2010.07.011zbMath1352.62048arXiv0910.0845OpenAlexW2008211390MaRDI QIDQ608320

Gordon Gudendorf, Johan Segers

Publication date: 25 November 2010

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0910.0845

zbMATH Keywords

empirical processlinear regressionordinary least squaresPickands dependence functionunit simplexminimum-variance estimatormultivariate extreme-value distribution

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Measures of association (correlation, canonical correlation, etc.) (62H20) Statistics of extreme values; tail inference (62G32) Functional limit theorems; invariance principles (60F17)

Related Items

Inference for asymptotically independent samples of extremes, Inference for Archimax copulas, Estimating multivariate extremal dependence: a new proposal, A novel positive dependence property and its impact on a popular class of concordance measures, A comparison of dependence function estimators in multivariate extremes, A goodness-of-fit test for bivariate extreme-value copulas, Nonparametric estimation of multivariate extreme-value copulas, Unnamed Item, On estimating extremal dependence structures by parametric spectral measures, Extreme value copula estimation based on block maxima of a multivariate stationary time series, Statistics for tail processes of Markov chains, A general approach to generate random variates for multivariate copulae, On weak conditional convergence of bivariate Archimedean and extreme value copulas, and consequences to nonparametric estimation, Multivariate nonparametric estimation of the Pickands dependence function using Bernstein polynomials, Non-linear models for extremal dependence, Nonparametric rank-based tests of bivariate extreme-value dependence, Bayesian model averaging for multivariate extremes, On the effect of long-range dependence on extreme value copula estimation with fixed marginals, Estimation of Pickands dependence function of bivariate extremes under mixing conditions, Extremes and regular variation, Total positivity of copulas from a Markov kernel perspective, Toward a Copula Theory for Multivariate Regular Variation, Nonparametric estimation of the conditional tail copula, Bayesian estimation of bivariate Pickands dependence function

Uses Software

• evd

Cites Work

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