DSpace
 
  Repositório Institucional da Universidade de Aveiro > Departamento de Matem├ítica > MAT - Artigos >
 Subsampling techniques and the Jackknife methodology in the estimation of the extremal index
Please use this identifier to cite or link to this item http://hdl.handle.net/10773/6130

title: Subsampling techniques and the Jackknife methodology in the estimation of the extremal index
authors: Gomes, M. Ivette
Hall, Andreia
Miranda, M. Cristina
keywords: Extreme value theory
Extremal index
issue date: 2008
publisher: Elsevier
abstract: For a sequence of independent, identically distributed random variables any limiting point process for the time normalized exceedances of high levels is a Poisson process. However, for stationary dependent sequences, under general local and asymptotic dependence restrictions, any limiting point process for the time normalized exceedances of high levels is a compound Poisson process, i.e., there is a clustering of high exceedances, where the underlying Poisson points represent cluster positions, and the multiplicities correspond to the cluster sizes. For such classes of stationary sequences there exists the extremal index theta, 0 <=theta <= 1, directly related to the clustering of exceedances of high values. The extremal index theta is equal to one for independent, identically distributed sequences, i.e., high exceedances appear individually, and theta>0 for "almost all" cases of interest. The estimation of the extremal index through the use of the Generalized Jackknife methodology, possibly together with the use of subsampling techniques, is performed. Case studies in the fields of environment and finance will illustrate the performance of the new extremal index estimator comparatively to the classical one. (C) 2007 Elsevier B.V. All rights reserved.
URI: http://hdl.handle.net/10773/6130
ISSN: 0167-9473
publisher version/DOI: http://dx.doi.org/10.1016/j.csda.2007.06.023
source: Computational statistics & data analysis
appears in collectionsMAT - Artigos

files in this item

file description sizeformat
COMSTA3756(GHallM).pdf910.27 kBAdobe PDFview/open
Restrict Access. You can Request a copy!
statistics

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! RCAAP OpenAIRE DeG├│is
ria-repositorio@ua.pt - Copyright ©   Universidade de Aveiro - RIA Statistics - Powered by MIT's DSpace software, Version 1.6.2