  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.
