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A New Class of Reduced-Bias Generalized Hill Estimators

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Resumo: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.
Autores principais:Henriques-Rodrigues, Lígia
Outros Autores:Caeiro, Frederico; Gomes, M. Ivette
Assunto:asymptotic properties extreme value index generalized means Monte Carlo simulation reduced-bias estimators statistics of extremes Computer Science (miscellaneous) General Mathematics Engineering (miscellaneous)
Ano:2024
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL

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