Estimation of extreme conditional quantiles under a general tail-first-order condition

Laurent Gardes, Armelle Guillou () and Claire Roman
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Laurent Gardes: Université de Strasbourg & CNRS
Armelle Guillou: Université de Strasbourg & CNRS
Claire Roman: Université de Strasbourg & CNRS

Annals of the Institute of Statistical Mathematics, 2020, vol. 72, issue 4, No 2, 915-943

Abstract: Abstract We consider the estimation of an extreme conditional quantile. In a first part, we propose a new tail condition in order to establish the asymptotic distribution of an extreme conditional quantile estimator. Next, a general class of estimators is introduced, which encompasses, among others, kernel or nearest neighbors types of estimators. A unified theorem of the asymptotic normality for this general class of estimators is provided under the new tail condition and illustrated on the different well-known examples. A comparison between different estimators belonging to this class is provided on a small simulation study and illustrated on a real dataset on earthquake magnitudes.

Keywords: Extreme quantile; Local estimation; Asymptotic normality (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10463-019-00713-7

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