Weighted least squares estimators for the Parzen tail index
Statistics Theory
2020-03-02 v1 Statistics Theory
Abstract
Estimation of the tail index of heavy-tailed distributions and its applications are essential in many research areas. We propose a class of weighted least squares (WLS) estimators for the Parzen tail index. Our approach is based on the method developed by \cite{Holan2010}. We investigate consistency and asymptotic normality of the WLS estimators. Through a simulation study, we make a comparison with the Hill, Pickands, DEdH (Dekkers, Einmahl and de Haan) and ordinary least squares (OLS) estimators using the mean square error as criterion. The results show that in a restricted model some members of the WLS estimators are competitive with the Pickands, DEdH and OLS estimators.
Keywords
Cite
@article{arxiv.2002.12631,
title = {Weighted least squares estimators for the Parzen tail index},
author = {Amenah AL-Najafi and László Viharos},
journal= {arXiv preprint arXiv:2002.12631},
year = {2020}
}