TALK by Aleksey POLUNCHENKO
January 24, 2013
State-of-the-Art in Sequential Change-Point Detection
Aleksey Polunchenko (Binghamton University)
The problem of sequential change-point detection is concerned with the design and analysis of fastest
ways to detect a change in the statistical profile of a random time process, given a tolerable risk of a false
detection. The subject finds applications, e.g., in quality and process control, anomaly and failure detection,
surveillance and security, finance, intrusion detection, boundary tracking, etc. We provide an overview of
the field's state-of-the-art. 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 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 field's best detection procedures at work.
This is joint work with Alexander G. Tartakovsky, Department of Mathematics and Center for
Applied Mathematical Sciences, University of Southern California.