Modeling extreme events: sample fraction adaptive choice in parameter estimation

Manuela Neves, Ivette Gomes, Fernanda Figueiredo, Dora Prata Gomes

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

1 Citation (Scopus)

Abstract

When modeling extreme events there are a few primordial parameters, among which we refer the extreme value index and the extremal index. The extreme value index measures the right tail-weight of the underlying distribution and the extremal index characterizes the degree of local dependence in the extremes of a stationary sequence. Most of the semi-parametric estimators of these parameters show the same type of behaviour: nice asymptotic properties, but a high variance for small values of k, the number of upper order statistics to be used in the estimation, and a high bias for large values of k. This shows a real need for the choice of k. Choosing some well-known estimators of those parameters we revisit the application of a heuristic algorithm for the adaptive choice of k. The procedure is applied to some simulated samples as well as to some real data sets.

Original languageEnglish
Title of host publicationNumerical Analysis and Applied Mathematics, ICNAAM 2012 - International Conference of Numerical Analysis and Applied Mathematics
EditorsT. E. Simos, G. Psihoyios, C. Tsitouras, Z. Anastassi
PublisherAmerican Institute of Physics
Pages1110-1113
Number of pages4
ISBN (Print)9780735410916
DOIs
Publication statusPublished - 1 Dec 2012
EventInternational Conference of Numerical Analysis and Applied Mathematics, ICNAAM 2012 - Kos, Greece
Duration: 19 Sep 201225 Sep 2012

Publication series

NameAIP Conference Proceedings
PublisherAmerican Institute of Physics Inc.
Volume1479
ISSN (Print)0094-243X

Conference

ConferenceInternational Conference of Numerical Analysis and Applied Mathematics, ICNAAM 2012
Country/TerritoryGreece
CityKos
Period19/09/1225/09/12

Keywords

  • adaptive choice
  • extremal index
  • extreme value index
  • sample fraction
  • semi-parametric estimation

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