Best Attainable Rates of Convergence for Estimators of the Stable Tail Dependence Function

✍ Scribed by Holger Drees; Xin Huang

• Publisher: Elsevier Science
• Year: 1998
• Tongue: English
• Weight: 478 KB
• Volume: 64
• Category: Article
• ISSN: 0047-259X
• DOI: 10.1006/jmva.1997.1708

✦ Synopsis

It is well known that a bivariate distribution belongs to the domain of attraction of an extreme value distribution G if and only if the marginals belong to the domain of attraction of the univariate marginal extreme value distributions and the dependence function converges to the stable tail dependence function of G. Hall and Welsh (1984, Ann. Statist. 12, 1079 1084) and Drees (1997b, Ann. Statist., to appear) addressed the problem of finding optimal rates of convergence for estimators of the extreme value index of an univariate distribution. The present paper deals with the corresponding problem for the stable tail dependence function. First an upper bound on the rate of convergence for estimators of the stable tail dependence function is established. Then it is shown that this bound is sharp by proving that it is attained by the tail empirical dependence function. Finally, we determine the limit distribution of this estimator if the dependence function satisfies a certain second-order condition.