The role of bootstrap in statistics of univariate extremes
Ivette Gomes – Universidade de Lisboa (Portugal)

The relevance of the bootstrap methodology in the reliable estimation of any parameter of extreme events is crucial, and a short overview of this methodology is provided. Bootstrapping, introduced in a 1979 pioneering article by Efron, is essentially a computer-intensive method for assigning measures of accuracy to sample estimates and to estimate the sampling distribution of almost any statistic using only very simple resampling methods, based on the observed value of the empirical distribution function. For an asymptotically consistent choice of the thresholds to use in the estimation of the extreme value index (EVI), we suggest and discuss single and double-bootstrap algorithms for the adaptive estimation of a positive EVI, the primary parameter in statistics of univariate extremes.


Ivette Gomes

Ivette Gomes was a Full Professor at the Department of Statistics and Operations Research, Faculty of Sciences, University of Lisbon (1988-2011), being now a principal researcher at the Centre for Statistics and Applications, University of Lisbon (CEAUL). She has a PhD in Statistics (University of Sheffield, UK, 1978) and a Habilitation Degree in Applied Mathematics (UL, 1982). One of her main areas of research is Statistics of Extremes. She was a founding member of the Portuguese Statistical Society and member of several scientific Associations. She has been involved in the organization of several international conferences, including the 56th Session of ISI, 2007. Among other editorial duties, she has been chief editor of Revstat, since 2003, and associate editor of Extremes since 2007. To know more…

 

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