标题： 子采样极值：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部指数估计器，它自然地通过计算子样本最大值而出现，该估计器属于Frechet吸引域的分布。 此估计器等价于在具有两个顺序统计量的Hill估计器上取U-统计量。 与Hill估计器相比，该估计器具有多重优势。 特别是，作为阈值k的函数，它的样本路径渐近平滑，使其比Hill估计器明显更稳定。 该估计器还允许一个简单且直观的阈值选择规则，无需拟合二阶模型。 《多元分析期刊》，130卷，2014年。 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. Journal of Multivariate Analysis, 130, 2014