HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

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

Gildas Mazo 1 Stéphane Girard 1 Florence Forbes 1
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
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.
Document type :
Journal articles
Complete list of metadata

Cited literature [35 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00979151
Contributor : Gildas Mazo Connect in order to contact the contributor
Submitted on : Thursday, October 22, 2015 - 10:10:55 AM
Last modification on : Tuesday, October 19, 2021 - 11:13:08 PM
Long-term archiving on: : Thursday, April 27, 2017 - 2:25:40 PM

File

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

Identifiers

Collections

Citation

Gildas Mazo, Stéphane 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

590

Files downloads

572