B0164
Title: Efficient change point detection and estimation in high-dimensional correlation matrices: Offline and online
Authors: Zhaoyuan Li - The Chinese University of Hong Kong, Shenzhen (China) [presenting]
Abstract: The problems of detecting a change point and estimating the location in the correlation matrices of a sequence of high-dimensional vectors are considered. Under the offline setting, a new break test is proposed based on sign flip parallel analysis to detect the existence of change points. Furthermore, a two-step approach combining a sign flip permutation dimension reduction step and a CUSUM statistic is proposed to estimate the change point's location and recover the support of changes. The consistency of the estimator is constructed. Simulation examples and real data applications illustrate the superior empirical performance of the proposed methods. The proposed methods outperform existing ones for non-Gaussian data and the change point in the extreme tail of a sequence and become more accurate as the dimension increases. In addition, the proposed methods are extended to online settings.
