Liyan Xie is an assistant professor in the School of Data Science at The Chinese University of Hong Kong, Shenzhen. She received her Ph.D. in Industrial Engineering (major in Statistics) from Georgia Institute of Technology in 2021. Her research interests lie in the intersection of statistics and optimization, with a primary focus on sequential change detection and their applications in sensor networks and spatio-temporal modeling. She was the finalist of the INFORMS QSR Student Paper Competition and Runner-up for the INFORMS Computing Society Student Paper Prize in 2019.
Talk: A new procedure for sequential change detection and applications
Abstract: We study the online change-point detection problem, where the underlying distribution of the streaming data changes from a known distribution to an alternative that is of a known parametric form but with unknown parameters. We propose a joint detection/estimation scheme, which we call Window-Limited CUSUM, that combines the cumulative sum (CUSUM) test with a sliding window-based consistent estimate of the post-change parameters. We characterize the optimal choice of the window size and show that the Window-Limited CUSUM still enjoys first-order asymptotic optimality, while being much more efficient in computation. A parallel variant is also proposed that facilitates the practical implementation of the test. We also present application examples in subspace detection, community detection, and spatio-temporal data.