论文标题
非平稳时间序列的顺序高斯近似
Sequential Gaussian approximation for nonstationary time series in high dimensions
论文作者
论文摘要
部分总和过程的高斯耦合是针对高维度$ d = o（n^{1/3}）$的。该耦合是用于独立随机向量的总和，然后扩展到非组织时间序列的总和。我们的不平等明确取决于维度和量度，因此也适用于随机向量的数组。为了实现高维统计推论，提出了可行的高斯近似方案。描述了对顺序测试和更改点检测的应用。
Gaussian couplings of partial sum processes are derived for the high-dimensional regime $d=o(n^{1/3})$. The coupling is derived for sums of independent random vectors and subsequently extended to nonstationary time series. Our inequalities depend explicitly on the dimension and on a measure of nonstationarity, and are thus also applicable to arrays of random vectors. To enable high-dimensional statistical inference, a feasible Gaussian approximation scheme is proposed. Applications to sequential testing and change-point detection are described.
