TY - JOUR
T1 - Homogeneity tests of covariance matrices with high-dimensional longitudinal data
AU - Zhong, Ping Shou
AU - Li, Runze
AU - Santo, Shawn
N1 - Funding Information:
We are grateful to the editor, associate editor and two referees for their insightful, constructive and careful comments that have significantly improved the paper. We thank Ms Amanda Applegate for her help with the English language. This research was supported by the U.S. National Science Foundation, National Institute of Drug Abuse and National Institutes of Health, and was partially supported by the National Natural Science Foundation of China.
Publisher Copyright:
©2019 Biometrika Trust.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - This paper deals with the detection and identification of changepoints among covariances of high-dimensional longitudinal data, where the number of features is greater than both the sample size and the number of repeated measurements. The proposed methods are applicable under general temporal-spatial dependence. A new test statistic is introduced for changepoint detection, and its asymptotic distribution is established. If a changepoint is detected, an estimate of the location is provided. The rate of convergence of the estimator is shown to depend on the data dimension, sample size, and signal-to-noise ratio. Binary segmentation is used to estimate the locations of possibly multiple changepoints, and the corresponding estimator is shown to be consistent under mild conditions. Simulation studies provide the empirical size and power of the proposed test and the accuracy of the changepoint estimator.An application to a time-course microarray dataset identifies gene sets with significant gene interaction changes over time.
AB - This paper deals with the detection and identification of changepoints among covariances of high-dimensional longitudinal data, where the number of features is greater than both the sample size and the number of repeated measurements. The proposed methods are applicable under general temporal-spatial dependence. A new test statistic is introduced for changepoint detection, and its asymptotic distribution is established. If a changepoint is detected, an estimate of the location is provided. The rate of convergence of the estimator is shown to depend on the data dimension, sample size, and signal-to-noise ratio. Binary segmentation is used to estimate the locations of possibly multiple changepoints, and the corresponding estimator is shown to be consistent under mild conditions. Simulation studies provide the empirical size and power of the proposed test and the accuracy of the changepoint estimator.An application to a time-course microarray dataset identifies gene sets with significant gene interaction changes over time.
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U2 - 10.1093/biomet/asz011
DO - 10.1093/biomet/asz011
M3 - Article
C2 - 31427823
AN - SCOPUS:85083564067
SN - 0006-3444
VL - 106
SP - 619
EP - 634
JO - Biometrika
JF - Biometrika
IS - 3
ER -