Revisiting Resampling Methods in the Extremal Index Estimation: Improving Risk Assessment

Dora Prata Gomes, M. Manuela Neves

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Extreme value theory is an area of primordial importance for modelling extreme risks, allowing to estimate and predict beyond the range of data available. Among several parameters of interest, the extremal index is a crucial parameter in a dependent set-up, characterizing the degree of local dependence in the extremes of a stationary sequence. Its estimation has been addressed by several authors but some difficulties still remain. Resampling computer intensive methodologies have been recently considered in a reliable estimation of parameters of rare events. However classical bootstrap cannot be applied and block bootstrap procedures need to be considered. The block size for resampling strongly affects the estimates and needs to be properly chosen. Here, procedures for the choice of the block size for resampling are revisited and an improvement of the methods used in previous works for that choice is also considered. A simulation study will illustrate the performance of the aforementioned procedures. A real application is also presented.
Original languageEnglish
Title of host publicationRecent Studies on Risk Analysis and Statistical Modeling
EditorsTeresa Oliveira, Christos Kitsos, Amílcar Oliveira, Luís Grilo
Place of PublicationCham
PublisherSpringer International Publishing AG
Chapter10
Pages141-153
Number of pages13
ISBN (Electronic)978-3-319-76604-1
ISBN (Print)978-3-319-76605-8
DOIs
Publication statusPublished - 2018

Publication series

NameContributions of statistics
PublisherSpringer International Publishing AG
ISSN (Print)1431-1968

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    Prata Gomes, D., & Neves, M. M. (2018). Revisiting Resampling Methods in the Extremal Index Estimation: Improving Risk Assessment. In T. Oliveira, C. Kitsos, A. Oliveira, & L. Grilo (Eds.), Recent Studies on Risk Analysis and Statistical Modeling (pp. 141-153). (Contributions of statistics). Cham: Springer International Publishing AG. https://doi.org/10.1007/978-3-319-76605-8_10