The Role of Bootstrap Methodologies in the Estimation of a Negative Extreme Value Index

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

Abstract

In this note we deal with the estimation, under a semi-parametric framework, of a negative extreme value index, the primary parameter in statistics of extremes. We consider a recent class of generalized negative moment estimators of a negative extreme value index. Apart from the usual integer parameter k, related to the number of top order statistics involved in the estimation, the estimator depend on an extra real parameter θ, which makes it highly flexible and possibly second-order unbiased for a large variety of models. We are interested on the study of the bootstrap method in Gomes et al. (2013) for the adaptive choice of the parameters.
Original languageUnknown
Title of host publicationProceedings 59th ISI World Statistics Congress
Pages103-108
Publication statusPublished - 1 Jan 2013
Event59th World Statistics Congress -
Duration: 1 Jan 2013 → …

Conference

Conference59th World Statistics Congress
Period1/01/13 → …

Cite this