Peaks-Over-Threshold Modeling Under Random Censoring

Recently, the topic of extreme value under random censoring has been considered. Different estimators for the index have been proposed (see Beirlant et al., 2007 Beirlant , J. , Guillou , A. , Dierckx , G. , Fils-Villetard , A. ( 2007 ). Estimation of the extreme value index and extreme quantiles under random censoring . Extremes 10 : 151 – 174 .Crossref] , Google Scholar]). All of them are constructed as the classical estimators (without censoring) divided by the proportion of non censored observations above a certain threshold. Their asymptotic normality was established by Einmahl et al. (2008 Einmahl , J. H. J. , Fils-Villetard , A. , Guillou , A. ( 2008 ). Statistics of extremes under random censoring . Bernoulli 14 ( 1 ): 207 – 227 . Google Scholar]). An alternative approach consists of using the Peaks-Over-Threshold method (Balkema and de Haan, 1974 Balkema , A. , de Haan , L. ( 1974 ). Residual life at great age . Ann. Probab. 2 : 792 – 804 .Crossref], Web of Science ®] , Google Scholar]; Smith, 1987 Smith , R. L. ( 1987 ). Estimating tails of probability distributions . Ann. Statist. 15 : 1174 – 1207 .Crossref], Web of Science ®] , Google Scholar]) and to adapt the likelihood to the context of censoring. This leads to ML-estimators whose asymptotic properties are still unknown. The aim of this article is to propose one-step approximations, based on the Newton-Raphson algorithm. Based on a small simulation study, the one-step estimators are shown to be close approximations to the ML-estimators. Also, the asymptotic normality of the one-step estimators has been established, whereas in case of the ML-estimators it is still an open problem. The proof of our result, whose approach is new in the Peaks-Over-Threshold context, is in the spirit of Lehmann's theory (1991 Lehmann , E. L. ( 1991 ). Theory of Point Estimation . Pacific Grove , CA : Wadsworth & Brooks/Cole Advanced Books & Software .Crossref] , Google Scholar]).