Zapisane w:
Opis bibliograficzny
Główni autorzy: Mancer, Saida, Necir, Abdelhakim, Meraghni, Djamel
Format: Preprint
Wydane: 2025
Hasła przedmiotowe:
Dostęp online:https://arxiv.org/abs/2507.15744
Etykiety: Dodaj etykietę
Nie ma etykietki, Dołącz pierwszą etykiete!
_version_ 1866909703071596544
author Mancer, Saida
Necir, Abdelhakim
Meraghni, Djamel
author_facet Mancer, Saida
Necir, Abdelhakim
Meraghni, Djamel
contents By introducing a weight function into the density power divergence, we develop a new class of robust and smooth estimators for the tail index of Pareto-type distributions, offering improved efficiency in the presence of outliers. These estimators can be viewed as a robust generalization of both weighted least squares and kernel-based tail index estimators. We establish the consistency and asymptotic normality of the proposed class. A simulation study is conducted to assess their finite-sample performance in comparison with existing methods.
format Preprint
id arxiv_https___arxiv_org_abs_2507_15744
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Robust and Smooth Estimation of the Extreme Tail Index via Weighted Minimum Density Power Divergence
Mancer, Saida
Necir, Abdelhakim
Meraghni, Djamel
Statistics Theory
62G32, 62G05, 62G20, 62G35
By introducing a weight function into the density power divergence, we develop a new class of robust and smooth estimators for the tail index of Pareto-type distributions, offering improved efficiency in the presence of outliers. These estimators can be viewed as a robust generalization of both weighted least squares and kernel-based tail index estimators. We establish the consistency and asymptotic normality of the proposed class. A simulation study is conducted to assess their finite-sample performance in comparison with existing methods.
title Robust and Smooth Estimation of the Extreme Tail Index via Weighted Minimum Density Power Divergence
topic Statistics Theory
62G32, 62G05, 62G20, 62G35
url https://arxiv.org/abs/2507.15744