A New Class of Reduced-Bias Generalized Hill Estimators

The estimation of the extreme value index (EVI) is a crucial task in the field of statistics of extremes, as it provides valuable insights into the tail behavior of a distribution. For models with a Pareto-type tail, the Hill estimator is a popular choice. However, this estimator is susceptible to b...

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Bibliographic Details
Published in:Mathematics (Basel) 2024-09, Vol.12 (18), p.2866
Main Authors: Henriques-Rodrigues, Lígia, Caeiro, Frederico, Gomes, M. Ivette
Format: Article
Language:English
Subjects:
asymptotic properties
Bias
Distribution (Probability theory)
extreme value index
Extreme values
generalized means
Mathematical research
Monte Carlo simulation
Random variables
reduced-bias estimators
Statistical methods
Statistics
statistics of extremes
Validity
Citations: Items that this one cites
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Summary:The estimation of the extreme value index (EVI) is a crucial task in the field of statistics of extremes, as it provides valuable insights into the tail behavior of a distribution. For models with a Pareto-type tail, the Hill estimator is a popular choice. However, this estimator is susceptible to bias, which can lead to inaccurate estimations of the EVI, impacting the reliability of risk assessments and decision-making processes. This paper introduces a novel reduced-bias generalized Hill estimator, which aims to enhance the accuracy of EVI estimation by mitigating the bias.
ISSN:2227-7390
2227-7390
DOI:10.3390/math12182866