Two sample tests for high-dimensional autocovariances
Two sample tests for high-dimensional autocovariances
复制标题
DOI：
10.1016/j.csda.2020.107067
复制
发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
Changryong Baek;K. Gates;Benjamin Leinwand;V. Pipiras
中科院分区：
文献类型：
--
作者：
Changryong Baek;K. Gates;Benjamin Leinwand;V. Pipiras
研究了两个独立的高维时间序列的自协方差相等性检验问题。基于各维度上的合适平均值的上限值或总和的测试改编自现有文献。介绍了另一种基于主成分分析（PCA）的检验方法，并对其进行了理论研究。对于具有多个个体高维序列的两个总体的自协方差相等性检验的设置也考虑了扩展。所提出的方法在模拟数据上进行了评估，所引入的主成分分析测试的性能总体上是优越的。一个应用程序使用功能磁共振成像数据从个人体验两种不同的情绪状态提供。
The problem of testing for the equality of autocovariances of two independent high-dimensional time series is studied. Tests based on the suprema or sums of suitable averages across the dimensions are adapted from the available literature. Another test based on principal component analysis (PCA) is introduced and studied in theory. An extension is also considered to the setting of testing for the equality of autocovariances of two populations, having multiple individual high-dimensional series from the two populations. The proposed methodologies are assessed on simulated data, with the performance of the introduced PCA testing being superior overall. An application using fMRI data from individuals experiencing two different emotional states is provided.