A new partially reduced-bias mean-of-order p class of extreme value index estimators

M. Ivette Gomes, M. Fátima Brilhante, Frederico Caeiro and Dinis Pestana

Computational Statistics & Data Analysis, 2015, vol. 82, issue C, 223-237

Abstract: A class of partially reduced-bias estimators of a positive extreme value index (EVI), related to a mean-of-order-p class of EVI-estimators, is introduced and studied both asymptotically and for finite samples through a Monte-Carlo simulation study. A comparison between this class and a representative class of minimum-variance reduced-bias (MVRB) EVI-estimators is further considered. The MVRB EVI-estimators are related to a direct removal of the dominant component of the bias of a classical estimator of a positive EVI, the Hill estimator, attaining as well minimal asymptotic variance. Heuristic choices for the tuning parameters p and k, the number of top order statistics used in the estimation, are put forward, and applied to simulated and real data.

Keywords: Bias estimation; Heavy right-tails; Heuristic methods; Optimal levels; Semi-parametric estimation; Statistics of extremes (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:82:y:2015:i:c:p:223-237

DOI: 10.1016/j.csda.2014.08.017

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