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• W1738595936 abstract "This article is devoted to the study of tail index estimation based on i.i.d. multivariate observations, drawn from a standard heavy-tailed distribution, that is, of which Pareto-like marginals share the same tail index. A multivariate central limit theorem for a random vector, whose components correspond to (possibly dependent) Hill estimators of the common tail index α, is established under mild conditions. We introduce the concept of (standard) heavy-tailed random vector of tail index α and show how this limit result can be used in order to build an estimator of α with small asymptotic mean squared error, through a proper convex linear combination of the coordinates. Beyond asymptotic results, simulation experiments illustrating the relevance of the approach promoted are also presented." @default.
• W1738595936 created "2016-06-24" @default.
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• W1738595936 date "2015-12-21" @default.
• W1738595936 modified "2023-10-18" @default.
• W1738595936 title "On tail index estimation based on multivariate data" @default.
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• W1738595936 doi "https://doi.org/10.1080/10485252.2015.1124105" @default.
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