Nonparametric estimation of the dependence function for a multivariate extreme value distribution

27Citations
Citations of this article
19Readers
Mendeley users who have this article in their library.

Abstract

Understanding and modeling dependence structures for multivariate extreme values are of interest in a number of application areas. One of the well-known approaches is to investigate the Pickands dependence function. In the bivariate setting, there exist several estimators for estimating the Pickands dependence function which assume known marginal distributions [J. Pickands, Multivariate extreme value distributions, Bull. Internat. Statist. Inst., 49 (1981) 859-878; P. Deheuvels, On the limiting behavior of the Pickands estimator for bivariate extreme-value distributions, Statist. Probab. Lett. 12 (1991) 429-439; P. Hall, N. Tajvidi, Distribution and dependence-function estimation for bivariate extreme-value distributions, Bernoulli 6 (2000) 835-844; P. Capéraà, A.-L. Fougères, C. Genest, A nonparametric estimation procedure for bivariate extreme value copulas, Biometrika 84 (1997) 567-577]. In this paper, we generalize the bivariate results to p-variate multivariate extreme value distributions with p ≥ 2. We demonstrate that the proposed estimators are consistent and asymptotically normal as well as have excellent small sample behavior. © 2006 Elsevier Inc. All rights reserved.

Cite

CITATION STYLE

APA

Zhang, D., Wells, M. T., & Peng, L. (2008). Nonparametric estimation of the dependence function for a multivariate extreme value distribution. Journal of Multivariate Analysis, 99(4), 577–588. https://doi.org/10.1016/j.jmva.2006.09.011

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free