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Title: High Dimensional Change Point Inference: Recent Developments and Extensions
Change point analysis aims to detect structural changes in a data sequence. It has always been an active research area since it was introduced in the 1950s. In modern statistical applications, however, high-throughput data with increasing dimensions are ubiquitous in fields ranging from economics, finance to genetics and engineering. For those problems, the earlier works are typically no longer applicable. As a result, the problem of testing a change point for high dimensional data sequences has been an important yet challenging task. In this paper, we first focus on models for at most one change point, and review recent state-of-art techniques for change point testing of high dimensional mean vectors and compare their theoretical properties. Based on that, we provide a survey of some extensions to general high dimensional parameters beyond mean vectors as well as strategies for testing multiple change points in high dimensions. Finally, we discuss some open problems for possible future research directions.  more » « less
Award ID(s):
2100729
PAR ID:
10345675
Author(s) / Creator(s):
Date Published:
Journal Name:
Journal of Multivariate Analysis
Volume:
188
ISSN:
0047-259X
Page Range / eLocation ID:
104833
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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