标题： 子抽样极值：从块最大值到平滑尾部估计 显示英文标题

标题： Subsampling Extremes: From Block Maxima to Smooth Tail Estimation

摘要： 我们研究了一种新的尾指数估计量，该估计量出现在Frechet吸引域中，通过计算子样本最大值自然产生。 该估计量等价于在Hill估计量上使用两个顺序统计量的U统计量。 该估计量相较于Hill估计量具有多个优势。 特别是，作为阈值k的函数，它具有渐近平滑的样本路径，使其比Hill估计量稳定得多。 该估计量还接受一个简单直观的阈值选择规则，而不需要拟合二阶模型。 显示英文摘要

摘要： We study a new estimator for the tail index of a distribution in the Frechet domain of attraction that arises naturally by computing subsample maxima. This estimator is equivalent to taking a U-statistic over a Hill estimator with two order statistics. The estimator presents multiple advantages over the Hill estimator. In particular, it has asymptotically smooth sample paths as a function of the threshold k, making it considerably more stable than the Hill estimator. The estimator also admits a simple and intuitive threshold selection rule that does not require fitting a second-order model.