Skip to Main content Skip to Navigation
Journal articles

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

Gildas Mazo 1 Stephane Girard 1 Florence Forbes 1
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : In this paper, we address the issue of estimating the parameters of general multivariate copulas, that is, copulas whose partial derivatives may not exist. To this aim, we consider a weighted least-squares estimator based on dependence coefficients, and establish its consistency and asymptotic normality. The estimator's performance on finite samples is illustrated on simulations and a real dataset.
Complete list of metadatas

Cited literature [35 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00979151
Contributor : Gildas Mazo <>
Submitted on : Thursday, October 22, 2015 - 10:10:55 AM
Last modification on : Thursday, May 28, 2020 - 3:32:46 AM
Document(s) archivé(s) le : Thursday, April 27, 2017 - 2:25:40 PM

File

paper-revised-5.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Gildas Mazo, Stephane Girard, Florence Forbes. Weighted least-squares inference for multivariate copulas based on dependence coefficients. ESAIM: Probability and Statistics, EDP Sciences, 2015, 19, pp.746 - 765. ⟨10.1051/ps/2015014⟩. ⟨hal-00979151v6⟩

Share

Metrics

Record views

736

Files downloads

593