论文标题
尾部索引的回归估计器
Regression estimators for the tail index
论文作者
论文摘要
我们提出了一类加权最小二乘估计器，以定期变化的上尾部的分配功能的尾部指数。我们的方法基于\ cite {Holan2010}为Parzen Tail索引开发的方法。证明了估计量的渐近正态性。通过模拟研究，使用均方误差作为标准，在帕累托和霍尔模型中比较了这些估计量。结果表明，加权最小二乘估计器比其他研究的估计值更好。
We propose a class of weighted least squares estimators for the tail index of a distribution function with a regularly varying upper tail. Our approach is based on the method developed by \cite{Holan2010} for the Parzen tail index. Asymptotic normality of the estimators is proved. Through a simulation study, these and earlier estimators are compared in the Pareto and Hall models using the mean squared error as criterion. The results show that the weighted least squares estimator is better than the other estimators investigated.
