Dr. Aleksey Polunchenko (USC)

Title: State-of-the-Art in Sequential Change-Point Detection

Abstract: We consider the problem of sequential change-point detection. This problem is concerned with the design and analysis of fastest ways to detect a change in the statistical prole of a random time process, given a tolerable risk of making false detection. The subject nds applications, e.g., in quality control, anomaly detection, failure detection, surveillance, process control, intrusion detection, boundary tracking, etc. We provide an overview of the state-of-the-art in the eld. The overview spans over all major formulations of the underlying optimization problem, namely, Bayesian, generalized Bayesian, and minimax. We pay particular attention to the latest advances made in each. Also, we link together the generalized Bayesian problem with multi-cyclic disorder detection in a stationary regime when the change occurs at a distant time horizon. We conclude with a case study to show the eld's best detection procedures at work.

This is joint work with Alexander G. Tartakovsky.