Colloquium- "Online Change-Point Detection in High Dimensional Data" with Dr. Jun Li from Kent State University
Jun Li, Ph.D.
Kent State University
Online Change-Point Detection in High-Dimensional Data
Meet-n-Greet: 2:30 p.m. (Virtual)
Talk: 3 p.m. (Virtual)
Host: Zheng Xu, Ph.D.
ABSTRACT:
We propose some new procedures to detect a change point in 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. Accuracy of the theoretical results is confirmed by simulation studies. The practical use of the proposed procedures is demonstrated by real data.
SPEAKER BIO:
Dr. Li is currently an associate professor at Department of Mathematical Sciences at Kent State University. He received his Ph.D. in Statistics from Iowa State University in 2013. His research interests are in high-dimensional data inference with application to biological studies, high-dimensional (online and offline) change point analysis, variable selection/feature selection for high dimensional data, and nonparametric method. Dr. Li performs as a PI in an NSF grant in 2019-2022 and as a multi-PD/PI in an NIH grant in 2020-2023. Dr. Li has multiple publications including three articles in Annals of Statistics.
