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

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

Research output: Contribution to journalArticle

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)223-227
Number of pages5
JournalComputational Statistics & Data Analysis
Volume82
DOIs
Publication statusPublished - 2015

Keywords

  • Bias estimation
  • Heavy right-tails
  • Heuristic methods
  • Optimal levels
  • Semi-parametric estimation
  • Statistics of extremes

Fingerprint Dive into the research topics of 'A new partially reduced-bias mean-of-order p class of extreme value index estimators'. Together they form a unique fingerprint.

Cite this