On the ghoudi, khoudraji, and rivest test for extreme-value dependence

Canadian Journal of Statistics

Volumn 37, Issue 4, 2009, Pages 534-552

  Ghorbal, Noomen Ben ^a   Christian, Genest ^a   Johanna, Nešlehová ^b  

^a UNIVERSITÉ LAVAL   (Canada)

^b ETH ZURICH   (Switzerland)

Author keywords

Copula; Extreme value; Jackknife; Kendall's tau; Probability integral transformation; Ranks; U statistic

Indexed keywords

EID: 74049141785     PISSN: 03195724     EISSN: None     Source Type: Journal    
DOI: 10.1002/cjs.10034     Document Type: Article

References (21)

1. B. Abdous, C. Genest & B. Rémillard (2005). Dependence properties of meta-elliptical distributions, in P. Duchesne & B. Rémillard, editors, Statistical Modeling and Analysis for Complex Data Problems, GERAD 25th Anniversary Series, Springer, New York, pp. 1-15.
2. B. Abdous & K. Ghoudi (2005). Non-parametric estimators of multivariate extreme dependence functions. Journal of Nonparametric Statistics, 17, 915-935.
3. A. Azzalini & A. Capitanio (2003). Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t-distribution. Journal of the Royal Statistical Society Series B, 65, 367-389.
4. P. Barbe, C. Genest, K. Ghoudi & B. Rémillard (1996). On Kendall's process. Journal of Multivariate Analysis, 58, 197-229.
5. R. D. Cook & M. E. Johnson (1986). Generalized Burr-Pareto-logisticdistributions with applications to a uranium exploration data set. Technometrics, 28, 123-131.
6. H.-B. Fang, K.-T. Fang & S. Kotz (2002). The meta-elliptical distributions with given marginals. Journal of Multivariate Analysis, 82, 1-16. [Corr.: Journal of Multivariate Analysis, 94, 222-223.]
7. A. Fils-Villetard, A. Guillou & J. Segers (2008). Projection estimators of Pickands dependence functions. The Canadian Journal of Statistics, 36, 369-382.
8. E. W. Frees & E. A. Valdez (1998). Understanding relationships using copulas. North American Actuarial Journal, 2, 1-25.
9. C. Genest, K. Ghoudi & L.-P. Rivest (1998). Comment on Understanding relationships using copulas, by E. W. Frees & E. A. Valdez. North American Actuarial Journal, 2, 143-149.
10. C. Genest, J.-F. Quessy & B. Rémillard (2006). Goodness-of-fit procedures for copula models based on the probability integral transformation. Scandinavian Journal of Statistics, 33, 337-366.
11. C. Genest & L.-P. Rivest (1993). Statistical inference procedures for bivariate Archimedean copulas. Journal of the American Statistical Association, 88, 1034-1043.
12. C. Genest & J. Segers (2009). Rank-based inference for bivariate extreme-value copulas. The Annals of Statistics, 37, 2990-3022.
13. K. Ghoudi, A. Khoudraji & L.-P. Rivest (1998). Propriétés statistiques des copules de valeurs extrêmes bidimensionnelles. The Canadian Journal of Statistics, 26, 187-197.
14. V. Grégoire, C. Genest & M. Gendron (2008). Using copulas to model price dependence in energy markets. Energy Risk, 5(5), 58-64.
15. S. Guillotte & F. Perron (2008). A Bayesian estimator for the dependence function of a bivariate extreme-value distribution. The Canadian Journal of Statistics, 36, 383-396.
16. S. A. Klugman & R. Parsa (1999). Fitting bivariate loss distributions with copulas. Insurance: Mathematics and Economics, 24, 139-148.
17. A. J. Lee (1990). U-Statistics: Theory and Practice, Marcel Dekker, New York.
18. R. B. Nelsen (2006). An Introduction to Copulas, 2nd ed., Springer, New York.
19. J. Pickands (1981). Multivariate extreme value distributions. Proceedings of the 43rd Session of the International Statistical Institute, Vol. 49(2), pp. 859-878.
20. J. A. Tawn (1988). Bivariate extreme value theory: Models and estimation. Biometrika, 75, 397-415.
21. A. van der Vaart (1998). Asymptotic Statistics, Cambridge University Press, New York.