Homogeneity tests of covariance matrices with high-dimensional longitudinal data.

ABSTRACT: 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 s

SUBMITTER: Zhong PS 

PROVIDER: S-EPMC6690172 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature