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Conference Papers Year : 2021

Dependence Testing via Extremes for Regularly Varying Models

Abstract

In heavy-tailed data, such as data drawn from regularly varying models, extreme values can occur relatively often. As a consequence, in the context of hypothesis testing, extreme values can provide valuable information in identifying dependence between two data sets. In this paper, the error exponent of a dependence test is studied when only processed data recording whether or not the value of the data exceeds a given value is available. An asymptotic approximation of the error exponent is obtained, establishing a link with the upper tail dependence, which is a key quantity in extreme value theory. While the upper tail dependence has been well characterized for elliptically distributed models, much less is known in the non-elliptical setting. To this end, a family of nonelliptical distributions with regularly varying tails arising from shot noise is studied, and an analytical expression for the upper tail dependence derived.
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Dates and versions

hal-03223444 , version 1 (11-05-2021)

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Malcolm Egan. Dependence Testing via Extremes for Regularly Varying Models. ISIT 2021 - IEEE International Symposium on Information Theory, Jul 2021, Melbourne, Australia. pp.1-6, ⟨10.1109/ISIT45174.2021.9518201⟩. ⟨hal-03223444⟩
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