CMStatistics 2021: Start Registration
View Submission - CMStatistics
Title: Online change-point detection for high-dimensional data Authors:  Jun Li - Kent State University (United States) [presenting]
Abstract: Some new procedures are proposed to detect a change for high-dimensional online data. Theoretical properties of the proposed procedures are explored in the high dimensional setting. More precisely, we derive their average run lengths (ARLs) when there is no change point, and expected detection delays (EDDs) when there is a change point. The accuracy of the theoretical results is confirmed by simulation studies. The practical use of the proposed procedures is demonstrated by real data.