Reduced bias Hill estimators

Ivanilda Cabral, Frederico Caeiro, M. Ivette Gomes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For heavy tails, classical extreme value index estimators, like the Hill estimator, are usually asymptotically biased. Consequently those estimators are quite sensitive to the number of top order statistics used in the estimation. The recent minimum-variance reduced-bias extreme value index estimators enable us to remove the dominant component of asymptotic bias and keep the asymptotic variance of the new estimators equal to the asymptotic variance of the Hill estimator. In this paper a new minimum-variance reduced-bias extreme value index estimator is introduced, and its non degenerate asymptotic behaviour is studied. A comparison with another important minimum-variance reduced-bias extreme value index estimator is also provided.

Original languageEnglish
Title of host publicationInternational Conference of Computational Methods in Sciences and Engineering 2016, ICCMSE 2016
PublisherAmerican Institute of Physics Inc.
Volume1790
ISBN (Electronic)978-0-7354-1454-9
DOIs
Publication statusPublished - 6 Dec 2016
EventInternational Conference of Computational Methods in Sciences and Engineering 2016, ICCMSE 2016 - Athens, Greece
Duration: 17 Mar 201620 Mar 2016

Conference

ConferenceInternational Conference of Computational Methods in Sciences and Engineering 2016, ICCMSE 2016
CountryGreece
CityAthens
Period17/03/1620/03/16

Keywords

  • Computer Science
  • Physics

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  • Cite this

    Cabral, I., Caeiro, F., & Gomes, M. I. (2016). Reduced bias Hill estimators. In International Conference of Computational Methods in Sciences and Engineering 2016, ICCMSE 2016 (Vol. 1790). [080006] American Institute of Physics Inc.. https://doi.org/10.1063/1.4968687