A New Regression-Based Tail Index Estimator
提出一种新的回归方法估计厚尾分布的尾指数，相比现有方法能减少偏差、对尾长选择不敏感，且在慢变函数影响慢或未知依赖形式下表现良好，同时给出了时间依赖和条件异方差下的渐近方差计算方法。
Abstract A new regression-based approach for the estimation of the tail index of heavy-tailed distributions with several important properties is introduced. First, it provides a bias reduction when compared to available regression-based methods; second, it is resilient to the choice of the tail length used for the estimation of the tail index; third, when the effect of the slowly varying function at infinity of the Pareto distribution vanishes slowly, it continues to perform satisfactorily; and fourth, it performs well under dependence of unknown form. An approach to compute the asymptotic variance under time dependence and conditional heteroskcedasticity is also provided.