Large-sample tests of extreme-value dependence for multivariate copulas

被引：32

作者：

Kojadinovic, Ivan ^{[1
]}

Segers, Johan ^{[2
]}

Yan, Jun ^{[3
]}

机构：

[1] Univ Pau & Pays Adour, CNRS, Lab Math & Applicat, UMR 5142, F-64013 Pau, France

[2] Catholic Univ Louvain, Inst Stat Biostat & Sci Actuarielles, B-1348 Louvain, Belgium

[3] Univ Connecticut, Dept Stat, Storrs, CT 06269 USA

来源：

CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE | 2011年 / 39卷 / 04期

关键词：

Max-stability; multiplier central limit theorem; pseudo-observations; ranks; 62H15; 62G32; 62G09; 62G30;

D O I：

10.1002/cjs.10110

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Starting from the characterization of extreme-value copulas based on max-stability, large-sample tests of extreme-value dependence for multivariate copulas are studied. The two key ingredients of the proposed tests are the empirical copula of the data and a multiplier technique for obtaining approximate p-values for the derived statistics. The asymptotic validity of the multiplier approach is established, and the finite-sample performance of a large number of candidate test statistics is studied through extensive Monte Carlo experiments for data sets of dimension two to five. In the bivariate case, the rejection rates of the best versions of the tests are compared with those of the test of Ghoudi et al. (1998) recently revisited by Ben Ghorbal et al. (2009). The proposed procedures are illustrated on bivariate financial data and trivariate geological data. The Canadian Journal of Statistics 39: 703720; 2011. (c) 2011 Statistical Society of Canada

引用

页码：703 / 720

页数：18

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