标题： 一种自归一化块抽样的统一方法 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过程在不同的模型假设下通常差异较大，因为部分和过程的表现取决于时间序列是短期还是长期依赖，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，并证明相应的块抽样方法可以在不需估计辅助参数{\em 先验}的情况下，为上述不同情况提供一种统一的推断方法。 通过蒙特卡罗模拟来展示其小样本性能。 实现该方法的 R 函数可从作者处获得。 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.