Weighted least squares estimators for the Parzen tail index

DOI10.1007/S10998-021-00403-ZarXiv2002.12631MaRDI QIDQ6335763

Publication date: 28 February 2020

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.

Mathematics Subject Classification ID

Central limit and other weak theorems (60F05) Statistics of extreme values; tail inference (62G32)

This page was built for publication: Weighted least squares estimators for the Parzen tail index

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6335763)