A carregar...
Publicação
A New Class of Reduced-Bias Generalized Hill Estimators
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 1.01 MB | Adobe PDF |
Orientador(es)
Resumo(s)
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.
Descrição
UIDB/MAT/04674/2020.
© 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
Palavras-chave
asymptotic properties extreme value index generalized means Monte Carlo simulation reduced-bias estimators statistics of extremes Computer Science (miscellaneous) General Mathematics Engineering (miscellaneous)