Generalized Jackknife-Based Estimators for Univariate Extreme-Value Modeling

In this article, we revisit the importance of the generalized jackknife in the construction of reliable semi-parametric estimates of some parameters of extreme or even rare events. The generalized jackknife statistic is applied to a minimum-variance reduced-bias estimator of a positive extreme value...

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Bibliographic Details
Published in:Communications in statistics. Theory and methods 2013-04, Vol.42 (7), p.1227-1245
Main Authors: Gomes, M. Ivette, Martins, M. João, Neves, M. Manuela
Format: Article
Language:English
Subjects:
Bias reduction
Error analysis
Estimators
Extreme value index
Extreme values
Generalized jackknife
Joining
Mathematical models
Mean square errors
Mean square values
Primary 62G32, 62E20
Regression analysis
Secondary 65C05
Semi-parametric estimation
Simulation
Statistics
Statistics of extremes
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Summary:In this article, we revisit the importance of the generalized jackknife in the construction of reliable semi-parametric estimates of some parameters of extreme or even rare events. The generalized jackknife statistic is applied to a minimum-variance reduced-bias estimator of a positive extreme value index-a primary parameter in statistics of extremes. A couple of refinements are proposed and a simulation study shows that these are able to achieve a lower mean square error. A real data illustration is also provided.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2012.725263