标题： 在长记忆下的重采样方法的有效性 显示英文标题

标题： On the validity of resampling methods under long memory

摘要： 对于长记忆时间序列，基于重采样的推断非常重要，因为渐近分布可能是非高斯的且在统计上难以确定。 然而由于强依赖性，建立重采样方法的有效性并不容易。 在本文中，我们推导了广义长记忆时间序列的两个有限块之间的典型相关性的有效界。 我们展示了如何利用这个界来建立长记忆下一般统计量的子抽样过程的渐近一致性。 它允许子样本大小$b$为$o(n)$，其中$n$是样本大小，无论记忆的强度如何。 然后，我们能够改进文献中的许多结果。 显示英文摘要

摘要： For long-memory time series, inference based on resampling is of crucial importance, since the asymptotic distribution can be non-Gaussian and is difficult to determine statistically. However due to the strong dependence, establishing the validity of resampling methods is nontrivial. In this paper, we derive an efficient bound for the canonical correlation between two finite blocks of a wide class of long-memory time series. We show how this bound can be applied to establish the asymptotic consistency of subsampling procedures for general statistics under long memory. It allows the subsample size $b$ to be $o(n)$, where $n$ is the sample size, irrespective of the strength of the memory. We are then able to improve many results found in the literature.