Asymptotic Comparison at Optimal Levels of Minimum-Variance Reduced-Bias Tail-Index Estimators

Research output: Chapter in Book/Report/Conference proceedingChapter


In this chapter we are interested in the asymptotic comparison of a set of semi-parametric minimum-variance reduced-bias tail-index estimators, at optimal levels and for a wide class of models. 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 tail-index estimators will jointly enable us to better estimate the tail index, the primary parameter of extreme events.
Original languageUnknown
Title of host publicationAdvances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications
Editorsda Silva, João Lita, Frederico Caeiro, Isabel Natário, Carlos A. Braumman
Place of PublicationBerlin
PublisherSpringer Berlin Heidelberg
ISBN (Print)978-3-642-34903-4 / 978-3-642-34904-1
Publication statusPublished - 1 Jan 2013

Publication series

NameStudies in Theoretical and Applied Statistics
PublisherSpringer Berlin Heidelberg

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