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Asymptotic comparison at optimal levels of reduced-bias extreme value index estimators



In this article we are interested in the asymptotic comparison, at optimal levels, of a set of semi-parametric reduced-bias extreme value (EV) index estimators, valid for a wide class of heavy-tailed models, underlying the available data. Again, as in the classical case, there is not any estimator that can always dominate the alternatives, but interesting clear-cut patterns are found. Consequently, and in practice, a suitable choice of a set of EV index estimators will jointly enable us to better estimate the EV index γ, the primary parameter of extreme events.