Weighted least-squares inference for multivariate copulas based on dependence coefficients

DOI10.1051/ps/2015014zbMath1392.62157OpenAlexW2291659314MaRDI QIDQ2786502

Stéphane Girard, Gildas Mazo, Florence Forbes

Publication date: 12 February 2016

Published in: ESAIM: Probability and Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1051/ps/2015014

zbMATH Keywords

weighted least-squaresmethod of momentspartial derivativesparametric inferencesingular componentdependence coefficientsmultivariate copulas

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items (4)

A flexible and tractable class of one-factor copulas ⋮ Archimedean-based Marshall-Olkin distributions and related dependence structures ⋮ Risk attribution and interconnectedness in the EU via CDS data ⋮ Copulas Based on Marshall–Olkin Machinery

Uses Software

• ismev

Cites Work

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• Large Sample Properties of Generalized Method of Moments Estimators
• Goodness-of-fit tests for copulas: A review and a power study
• A flexible and tractable class of one-factor copulas
• Factor copula models for multivariate data
• Asymptotics of empirical copula processes under non-restrictive smoothness assumptions
• Nonparametric estimation of multivariate extreme-value copulas
• When uniform weak convergence fails: empirical processes for dependence functions and residuals via epi- and hypographs
• New estimators of the Pickands dependence function and a test for extreme-value dependence
• A goodness-of-fit test for multivariate multiparameter copulas based on multiplier central limit theorems
• An M-estimator for tail dependence in arbitrary dimensions
• An introduction to copulas.
• Rank-based inference for bivariate extreme-value copulas
• On the limiting behavior of the Pickands estimator for bivariate extreme- value distributions
• Kendall distribution functions.
• Distribution and dependence-function estimation for bivariate extreme-value distributions.
• Tests of independence and randomness based on the empirical copula process
• ESTIMATORS BASED ON KENDALL'S TAU IN MULTIVARIATE COPULA MODELS
• Copula Structure Analysis
• Statistical Inference Procedures for Bivariate Archimedean Copulas
• A continuous general multivariate distribution and its properties
• A nonparametric estimation procedure for bivariate extreme value copulas
• A semiparametric estimation procedure of dependence parameters in multivariate families of distributions
• Simulated Method of Moments Estimation for Copula-Based Multivariate Models
• Semiparametric estimation in copula models
• A Class of Statistics with Asymptotically Normal Distribution
• An introduction to statistical modeling of extreme values

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