A class of weighted Hill estimators. Issue 6 (3rd May 2021)

• Record Type: Journal Article
• Authors: Caeiro, Frederico; Mateus, Ayana; Soltane, Louiza
• Is Part Of: Computational and mathematical methods. Volume 3:Issue 6(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-03
• Subjects: asymptotic bias -- extreme value index -- semi‐parametric estimation -- Statistic of Extremes; Mathematics -- Data processing -- Periodicals; Numerical analysis -- Periodicals; Numerical analysis; Mathematics -- Data processing; Periodicals; 004.0151
• DOI: 10.1002/cmm4.1167
• Languages: English
• ISSNs: 2577-7408

Abstract

Abstract: In Statistics of Extremes, the estimation of the extreme value index is an essential requirement for further tail inference. In this work, we deal with the estimation of a strictly positive extreme value index from a model with a Pareto‐type right tail. Under this framework, we propose a new class of weighted Hill estimators, parameterized with a tuning parameter a . We derive their non‐degenerate asymptotic behavior and analyze the influence of the tuning parameter in such result. Their finite sample performance is analyzed through a Monte Carlo simulation study. A comparison with other important extreme value index estimators from the literature is also provided.