Work

A New Class of Reduced-Bias Generalized Hill Estimators

Year: 2024

Type: article

Abstract: 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.

Source: Mathematics

DOI: 10.3390/math12182866

Authors: Lígia Henriques-Rodrigues, Frederico Caeiro, M. Ivette Gomes

Institutions: University of Évora, Universidade Nova de Lisboa, University of Lisbon, Centro de Estatística e Aplicações

Language: English

Topic: Probability and Risk Models

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,955

Funder: Fundação para a Ciência e a Tecnologia