Nonparametric rank-based tests of bivariate extreme-value dependence

DOI10.1016/j.jmva.2010.05.004zbMath1201.62056OpenAlexW2091111644MaRDI QIDQ990906

Ivan Kojadinovic, Jun Yan

Publication date: 1 September 2010

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2010.05.004

zbMATH Keywords

rankscontiguitypseudo-observationsmultiplier central limit theoremextreme-value copulaslocal power comparisons

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Nonparametric estimation (62G05) Hypothesis testing in multivariate analysis (62H15) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Statistics of extreme values; tail inference (62G32) Monte Carlo methods (65C05)

Related Items

A general framework for testing homogeneity hypotheses about copulas, A Non-parametric Test of Exchangeability for Extreme-Value and Left-Tail Decreasing Bivariate Copulas, Some copula inference procedures adapted to the presence of ties, A moment-based test for extreme-value dependence, A modeler's guide to extreme value software, On the tail dependence in bivariate hydrological frequency analysis, Weighted estimation of the dependence function for an extreme-value distribution, Asymptotics of empirical copula processes under non-restrictive smoothness assumptions, Nonparametric estimation of multivariate extreme-value copulas, Discussion: Statistical models and methods for dependence in insurance data, Rejoinder: Statistical models and methods for dependence in insurance data, New estimators of the Pickands dependence function and a test for extreme-value dependence, Using B-splines for nonparametric inference on bivariate extreme-value copulas, Nonparametric inference for max-stable dependence, On nonparametric tests of multivariate meta-ellipticity, Graphical and formal statistical tools for the symmetry of bivariate copulas

Uses Software

• copula
• TwoCop
• copula

Cites Work

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