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April 2004 Bivariate tail estimation: dependence in asymptotic independence
Gerrit Draisma, Holger Drees, Ana Ferreira, Laurens De Haan
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Bernoulli 10(2): 251-280 (April 2004). DOI: 10.3150/bj/1082380219

Abstract

In the classical setting of bivariate extreme value theory, the procedures for estimating the probability of an extreme event are not applicable if the componentwise maxima of the observations are asymptotically independent. To cope with this problem, Ledford and Tawn proposed a submodel in which the penultimate dependence is characterized by an additional parameter. We discuss the asymptotic properties of two estimators for this parameter in an extended model. Moreover, we develop an estimator for the probability of an extreme event that works in the case of asymptotic independence as well as in the case of asymptotic dependence, and prove its consistency.

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Gerrit Draisma. Holger Drees. Ana Ferreira. Laurens De Haan. "Bivariate tail estimation: dependence in asymptotic independence." Bernoulli 10 (2) 251 - 280, April 2004. https://doi.org/10.3150/bj/1082380219

Information

Published: April 2004
First available in Project Euclid: 19 April 2004

zbMATH: 1058.62043
MathSciNet: MR2046774
Digital Object Identifier: 10.3150/bj/1082380219

Rights: Copyright © 2004 Bernoulli Society for Mathematical Statistics and Probability

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Vol.10 • No. 2 • April 2004
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