论文标题
加权的最小二乘估计器的Parzen尾部指数
Weighted least squares estimators for the Parzen tail index
论文作者
论文摘要
在许多研究领域中，重型分布及其应用的尾部指数的估计是必不可少的。我们建议使用Parzen尾部指数的一类加权最小二乘（WLS）估计器。我们的方法基于\ cite {Holan2010}开发的方法。我们研究了WLS估计量的一致性和渐近正态性。通过一项模拟研究，我们将使用均方根误差作为标准进行了与山丘，Pickands，Dedh（Dekkers，Einmahl和De Haan）和普通最小二乘（OLS）估计量进行比较。结果表明，在受限模型中，WLS估计器的一些成员与PickAnds，Dedh和OLS估计器具有竞争力。
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.
