On Blocking Rules for the Bootstrap with Dependent Data
研究了块自助法在依赖数据中如何选择最优块大小，发现最优块大小取决于具体任务，如方差估计时为n^(1/3)，单侧分布函数估计时为n^(1/4)，双侧分布函数估计时为n^(1/5)，并给出了直观解释和实用选择规则。
We address the issue of optimal block choice in applications of the block bootstrap to dependent data. It is shown that optimal block size depends significantly on context, being equal to n1/3, n1/4 and n1/5 in the cases of variance or bias estimation, estimation of a onesided distribution function, and estimation of a two-sided distribution function, respectively. A clear intuitive explanation of this phenomenon is given, together with outlines of theoretical arguments in specific cases. It is shown that these orders of magnitude of block sizes can be used to produce a simple, practical rule for selecting block size empirically. That technique is explored numerically.