Adaptive choice and resampling techniques in extremal index estimation

Dora Prata Gomes, M. Manuela Neves

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This work deals with the application of resampling techniques together with the adaptive choice of a ‘tuning’ parameter, the block size, b, to be used in the bootstrap estimation of the extremal index, that is a key parameter in extreme value theory in a dependent setup. Its estimation has been considered by many authors but some challenges still remain. One of these is the choice of the number of upper order statistics to be considered in the semiparametric estimation. Block-bootstrap and Jackknife-After-Bootstrap are two computational procedures applied here for improving the behavior of the extremal index estimators through an adaptive choice of the block size for the resampling procedure. A few results of a simulation study will be presented.

Original languageEnglish
Title of host publicationTheory and Practice of Risk Assessment
EditorsC. Kitsos , T. Oliveira, A. Rigas, S. Gulati
Place of PublicationCham
PublisherSpringer New York LLC
Number of pages12
ISBN (Electronic)978-3-319-18029-8
ISBN (Print)978-3-319-18028-1
Publication statusPublished - 2015
Event5th International Conference on Risk Analysis, ICRA5 2013 - Tomar, Portugal
Duration: 30 May 20131 Jun 2013

Publication series

NameSpringer Proceedings in Mathematics and Statistics
PublisherSpringer New York LLC
ISSN (Print)2194-1009


Conference5th International Conference on Risk Analysis, ICRA5 2013


  • Adaptive choice
  • Block size
  • Exterme value theory
  • External index
  • Resampling techniques


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