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In this paper, we propose a reduced-bias estimator of the EVI for Pareto-typetails (heavy-tailed) distributions. This is derived using the weighted leastsquares method. It is shown that the estimator is unbiased, consistent andasymptotically normal under the second-order conditions on the underlyingdistribution of the data. The finite sample properties of the proposedestimator are studied through a simulation study. The results show that it iscompetitive to the existing estimators of the extreme value index in terms ofbias and Mean Square Error. In addition, it yields estimates of $\gamma>0$ thatare less sensitive to the number of top-order statistics, and hence, can beused for selecting an optimal tail fraction. The proposed estimator is furtherillustrated using practical datasets from pedochemical and insurance.

A Reduced-Bias Weighted least square estimation of the Extreme Value Index