Estimation of the Bias of the Maximum Likelihood Estimators in an Extreme Value Context

Interest is centered on the maximum likelihood (ML) estimators of the parameters of the Generalized Pareto Distribution in an extreme value context. Our aim consists of reducing the bias of these estimates for which no explicit expression is available. To circumvent this difficulty, we prove that these estimators are asymptotically equivalent to one-step estimators introduced by Beirlant et al. (2010 Beirlant , J. , Guillou , A. , Toulemonde , G. ( 2010 ). Peaks-over-threshold modeling under random censoring . Commun. Statist. Theor. Meth.  [Google Scholar]) in a right-censoring context. Then, using this equivalence property, we estimate the bias of these one-step estimators to approximate the asymptotic bias of the ML-estimators. Finally, a small simulation study and an application to a real data set are provided to illustrate that these new estimators actually exhibit reduced bias.