A0166
Title: Multiple change point detection for high-dimensional data
Authors: Wenbiao Zhao - Beijing Institute of Technology (China) [presenting]
Lixing Zhu - Beijing Normal University (China)
Abstract: Simultaneously detecting multiple change points are investigated for high-dimensional data with dimensions that can be of an exponential rate of the sample size. The proposed estimation approach utilizes a signal statistic that is based on a sequence of local $U$-statistics, no matter whether the data have a sparse or dense structure. Both expensive computations that exhaustive search algorithms need and false positives that hypothesis testing-based approaches have to control can be avoided. The estimation consistency can hold for the locations and number of change points even when the number of change points diverges at a certain rate as the sample size goes to infinity. Further, because of its visualization nature, in practice, plotting the signal statistic can greatly help identify the locations in contrast to existing methods. The numerical studies are conducted to examine its performance in finite sample scenarios and a real data example is analyzed for illustration.
