标题： 一种统一的自归一化块抽样方法 显示英文标题

标题： A unified approach to self-normalized block sampling

摘要： 平稳时间序列均值的推断程序在不同的模型假设下通常是相当不同的，因为部分和过程的行为取决于时间序列是短距离依赖还是长距离依赖，或者其边缘分布是轻尾还是重尾。 在本文中，我们发展了自归一化块抽样的渐近理论，并证明了相应的块抽样方法可以在上述不同情况下提供一种统一的推断方法，即它不需要辅助参数的{\em 先验}估计。 蒙特卡罗模拟展示了其有限样本下的表现。 实现该方法的 R 函数可向作者索取。 显示英文摘要

摘要： The inference procedure for the mean of a stationary time series is usually quite different under various model assumptions because the partial sum process behaves differently depending on whether the time series is short or long-range dependent, or whether it has a light or heavy-tailed marginal distribution. In the current paper, we develop an asymptotic theory for the self-normalized block sampling, and prove that the corresponding block sampling method can provide a unified inference approach for the aforementioned different situations in the sense that it does not require the {\em a priori} estimation of auxiliary parameters. Monte Carlo simulations are presented to illustrate its finite-sample performance. The R function implementing the method is available from the authors.