Methodology and Computing in Applied Probability

, Volume 14, Issue 3, pp 649–684 | Cite as

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

  • Aleksey S. Polunchenko
  • Alexander G. Tartakovsky


We provide an overview of the state-of-the-art in the area of sequential change-point detection assuming discrete time and known pre- and post-change distributions. 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 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 two case studies to illustrate the cutting edge of the field at work.


CUSUM chart Quickest change detection Sequential analysis Sequential change-point detection Shiryaev’s procedure Shiryaev–Roberts procedure Shiryaev–Roberts–Pollak procedure Shiryaev–Roberts–r procedure 

AMS 2000 Subject Classifications

62L10 60G40 62C10 62C20 


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Aleksey S. Polunchenko
    • 1
  • Alexander G. Tartakovsky
    • 1
  1. 1.Department of MathematicsUniversity of Southern CaliforniaLos AngelesUSA

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