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

翻译：在本文中,我们建议对帕雷托型尾巴(重尾尾尾尾巴)分布的 EVI 降位估计值,这是使用加权最小方块方法推算的,表明根据数据基本分布的第二阶线条件,估计值是不带偏见的、一致的和无影响正常的。通过模拟研究,对拟议估计值的有限样本特性进行了研究。结果显示,从偏差和中平方错误的角度来看,它与现有极端值指数估计值具有竞争力。此外,它得出的估计值$\gamma>0,对上阶统计数字不那么敏感,因此可用于选择最佳尾部部分。还用来自精化和保险的实用数据集进一步说明拟议的估计值。