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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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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description 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.
doi_str_mv 10.1080/03610926.2012.725263
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source Taylor and Francis Science and Technology Collection
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
title Generalized Jackknife-Based Estimators for Univariate Extreme-Value Modeling
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