A1268
Title: Multiple change point detection for high-dimensional data
Authors: Wenbiao Zhao - Beijing Institute of Technology (China) [presenting]
Lixing Zhu - Beijing Normal University (China)
Falong Tan - Hong Kong Baptist University (Hong Kong)
Abstract: The purpose is to investigate simultaneously detecting multiple change points of the high-dimensional data, with either sparse or dense structure, that the dimension can be of the exponential rate of the sample size. The proposed estimation approach utilizes a signal statistic based on a sequence of signal screening-based local U-statistics. It can avoid both expensive computations that exhaustive search algorithms need and false positives that hypothesis testing-based approaches have to control. 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 in the literature. Numerical studies are conducted to examine its performance in finite sample scenarios, and a real data example is analyzed for illustration.
