Advances in Applied Probability

Asymptotic dependence for light-tailed homothetic densities

Guus Balkema and Natalia Nolde
Source: Adv. in Appl. Probab. Volume 44, Number 2 (2012), 506-527.

Abstract

Dependence between coordinate extremes is a key factor in any multivariate risk assessment. Hence, it is of interest to know whether the components of a given multivariate random vector exhibit asymptotic independence or asymptotic dependence. In the latter case the structure of the asymptotic dependence has to be clarified. In the multivariate setting it is common to have an explicit form of the density rather than the distribution function. In this paper we therefore give criteria for asymptotic dependence in terms of the density. We consider distributions with light tails and restrict attention to continuous unimodal densities defined on the whole space or on an open convex cone. For simplicity, the density is assumed to be homothetic: all level sets have the same shape. Balkema and Nolde (2010) contains conditions on the shape which guarantee asymptotic independence. The situation for asymptotic dependence, treated in the present paper, is more delicate.

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Primary Subjects: 60G55, 60G70, 62E20
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Links and Identifiers

Permanent link to this document: http://projecteuclid.org/euclid.aap/1339878722
Digital Object Identifier: doi:10.1239/aap/1339878722
Zentralblatt MATH identifier: 06055132
Mathematical Reviews number (MathSciNet): MR2977406

References

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Mathematical Reviews (MathSciNet): MR2372552
Zentralblatt MATH: 1121.91055
Balkema, A. A. and Nolde, N. (2010). Asymptotic independence for unimodal densities. Adv. Appl. Prob. 42, 411–432.
Mathematical Reviews (MathSciNet): MR2675110
Zentralblatt MATH: 05735964
Digital Object Identifier: doi:10.1239/aap/1275055236
Project Euclid: euclid.aap/1275055236
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Mathematical Reviews (MathSciNet): MR900810
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Mathematical Reviews (MathSciNet): MR115241
Zentralblatt MATH: 0095.33703
Digital Object Identifier: doi:10.1007/BF01682329

2013 © Applied Probability Trust

Advances in Applied Probability

Advances in Applied Probability