TY - JOUR
T1 - Two sample tests for high-dimensional autocovariances
AU - Baek, Changryong
AU - Gates, Katheleen M.
AU - Leinwand, Benjamin
AU - Pipiras, Vladas
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2021/1
Y1 - 2021/1
N2 - 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.
AB - 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.
KW - Autocovariances
KW - Block multiplier bootstrap
KW - Dynamic factor models
KW - High-dimensional time series
KW - Hypothesis tests
KW - Principal components
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U2 - 10.1016/j.csda.2020.107067
DO - 10.1016/j.csda.2020.107067
M3 - Article
AN - SCOPUS:85090010009
SN - 0167-9473
VL - 153
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
M1 - 107067
ER -