A method of moments estimator of tail dependence

DOI10.3150/08-BEJ130zbMath1155.62017arXiv0710.2039OpenAlexW2171205854MaRDI QIDQ1002534

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

Publication date: 2 March 2009

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0710.2039

zbMATH Keywords

asymptotic propertiesmultivariate extremesconfidence regionsmethod of momentsgoodness-of-fit testtail dependencemeta-elliptical distribution

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Estimation in multivariate analysis (62H12) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Point estimation (62F10) Statistics of extreme values; tail inference (62G32)

Related Items (15)

Maximum empirical likelihood estimation of the spectral measure of an extreme-value distribu\-tion ⋮ Dense classes of multivariate extreme value distributions ⋮ Estimating a bivariate tail: a copula based approach ⋮ A method of moments estimator of tail dependence in meta-elliptical models ⋮ On the tail dependence in bivariate hydrological frequency analysis ⋮ Multiplier bootstrap of tail copulas with applications ⋮ Tail density estimation for exploratory data analysis using kernel methods ⋮ Statistical models and methods for dependence in insurance data ⋮ An M-estimator for tail dependence in arbitrary dimensions ⋮ The min-characteristic function: characterizing distributions by their min-linear projections ⋮ Parametric and non-parametric estimation of extreme earthquake event: the joint tail inference for mainshocks and aftershocks ⋮ Polynomial Pickands functions ⋮ Rank-based estimation under asymptotic dependence and independence, with applications to spatial extremes ⋮ Asymptotically distribution-free goodness-of-fit testing for tail copulas ⋮ Bias correction in multivariate extremes

Uses Software

• ismev

Cites Work

• Weighted approximations of tail copula processes with application to testing the bivariate extreme value condition
• The meta-elliptical distributions with given marginals
• Estimating the tail dependence function of an elliptical distribution
• Parametric tail copula estimation and model testing
• Best attainable rates of convergence for estimators of the stable tail dependence function
• Weak convergence and empirical processes. With applications to statistics
• Semi‐Parametric Models for the Multivariate Tail Dependence Function – the Asymptotically Dependent Case
• Statistics for near independence in multivariate extreme values
• Asymptotic Statistics
• Multivariate extremes, aggregation and dependence in elliptical distributions
• Statistics of Extremes
• Functional central limit theorems for processes with positive drift and their inverses
• An introduction to statistical modeling of extreme values
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