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Article Dans Une Revue Annals of the Institute of Statistical Mathematics Année : 2020

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

Résumé

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's 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.
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Dates et versions

hal-01818521 , version 1 (19-06-2018)

Identifiants

Citer

Laurent Gardes, Armelle Guillou, Claire Roman. Estimation of extreme conditional quantiles under a general tail first order condition. Annals of the Institute of Statistical Mathematics, 2020, 72, pp.915-943. ⟨10.1007/s10463-019-00713-7⟩. ⟨hal-01818521⟩
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