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Domination of sample maxima and related extremal dependence measures

Domination of sample maxima and related extremal dependence measures AbstractFor a given d-dimensional distribution function (df) H we introduce the class of dependence measures μ(H, Q) = −E{n H(Z1, . . . , Zd)}, where the random vector (Z1, . . . , Zd) has df Q which has the same marginal dfs as H. If both H and Q are max-stable dfs, we show that for a df F in the max-domain of attraction of H, this dependence measure explains the extremal dependence exhibited by F. Moreover, we prove that μ(H, Q) is the limit of the probability that the maxima of a random sample from F is marginally dominated by some random vector with df in the max-domain of attraction of Q. We show a similar result for the complete domination of the sample maxima which leads to another measure of dependence denoted by λ(Q, H). In the literature λ(H, H), with H a max-stable df, has been studied in the context of records, multiple maxima, concomitants of order statistics and concurrence probabilities. It turns out that both μ(H, Q) and λ(Q, H) are closely related. If H is max-stable we derive useful representations for both μ(H, Q) and λ(Q, H). Our applications include equivalent conditions for H to be a product df and F to have asymptotically independent components. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Dependence Modeling de Gruyter

Domination of sample maxima and related extremal dependence measures

Dependence Modeling , Volume 6 (1): 14 – May 1, 2016

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Publisher
de Gruyter
Copyright
© 2018 Enkelejd Hashorva, published by De Gruyter
ISSN
2300-2298
eISSN
2300-2298
DOI
10.1515/demo-2018-0005
Publisher site
See Article on Publisher Site

Abstract

AbstractFor a given d-dimensional distribution function (df) H we introduce the class of dependence measures μ(H, Q) = −E{n H(Z1, . . . , Zd)}, where the random vector (Z1, . . . , Zd) has df Q which has the same marginal dfs as H. If both H and Q are max-stable dfs, we show that for a df F in the max-domain of attraction of H, this dependence measure explains the extremal dependence exhibited by F. Moreover, we prove that μ(H, Q) is the limit of the probability that the maxima of a random sample from F is marginally dominated by some random vector with df in the max-domain of attraction of Q. We show a similar result for the complete domination of the sample maxima which leads to another measure of dependence denoted by λ(Q, H). In the literature λ(H, H), with H a max-stable df, has been studied in the context of records, multiple maxima, concomitants of order statistics and concurrence probabilities. It turns out that both μ(H, Q) and λ(Q, H) are closely related. If H is max-stable we derive useful representations for both μ(H, Q) and λ(Q, H). Our applications include equivalent conditions for H to be a product df and F to have asymptotically independent components.

Journal

Dependence Modelingde Gruyter

Published: May 1, 2016

References