Estimation of extreme quantiles from heavy and light tailed distributions

Jonathan El Methni 1 Laurent Gardes 2 Stephane Girard 1 Armelle Guillou 2
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 IRMA - Institut de Recherche Mathématique Avancée
Abstract : In Gardes et al. (2011), a new family of distributions is introduced, depending on two parameters tau and theta which encompasses Pareto-type distributions as well as Weibull tail-distributions. Estimators for theta and extreme quantiles are also proposed, but they both depend on the unknown parameter tau, making them useless in practical situations. In this paper, we propose an estimator of tau which is independent of theta. Plugging our estimator of tau in the two previous ones allows us to estimate extreme quantiles from Pareto-type and Weibull tail-distributions in an unifi ed way. The asymptotic distributions of our three new estimators are established and their efficiency is illustrated on a small simulation study and on a real data set.
Keywords : Weibull tail-distributions Pareto-type distributions Extreme quantile Maximum domain of attraction Asymptotic normality
Type de document :
Article dans une revue
Journal of Statistical Planning and Inference, Elsevier, 2012, 142 (10), pp.2735-2747. 〈10.1016/j.jspi.2012.03.025〉
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Soumis le : jeudi 19 avril 2012 - 08:52:11
Dernière modification le : mercredi 11 avril 2018 - 01:59:18
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Jonathan El Methni, Laurent Gardes, Stephane Girard, Armelle Guillou. Estimation of extreme quantiles from heavy and light tailed distributions. Journal of Statistical Planning and Inference, Elsevier, 2012, 142 (10), pp.2735-2747. 〈10.1016/j.jspi.2012.03.025〉. 〈hal-00627964v3〉

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