Computational Study of the Adaptive Estimation of the Extreme Value Index with Probability Weighted Moments

Frederico Caeiro, M. Ivette Gomes

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

1 Citation (Scopus)

Abstract

In statistics of extremes, the estimation of the extreme value index (EVI) is an important and central topic of research. We consider the probability weighted moment estimator of the EVI, based on the largest observations. Due to the specificity of the properties of the estimator, a direct estimation of the threshold is not straightforward. In this work, we consider an adaptive choice of the number of order statistics based on the double bootstrap methodology. Computational and empirical properties of the methodology are here provided.

Original languageEnglish
Title of host publicationRecent Developments in Statistics and Data Science
Subtitle of host publicationSPE2021, Évora, Portugal, October 13–16
EditorsRegina Bispo, Lígia Henriques-Rodrigues, Russell Alpizar-Jara, Miguel de Carvalho
Place of PublicationCham
PublisherSpringer
Pages29-39
Number of pages11
ISBN (Electronic)978-3-031-12766-3
ISBN (Print)978-3-031-12765-6
DOIs
Publication statusPublished - 29 Nov 2022
Event25th Congress of the Portuguese Statistical Society, SPE 2021 - Virtual, Online
Duration: 13 Oct 202116 Oct 2021

Publication series

NameSpringer Proceedings in Mathematics and Statistics
PublisherSpringer
Volume398
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017

Conference

Conference25th Congress of the Portuguese Statistical Society, SPE 2021
CityVirtual, Online
Period13/10/2116/10/21

Keywords

  • Bootstrap
  • Extreme value index
  • Heavy tails
  • Probability weighted moment
  • Semi-parametric estimation

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