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

Author

Listed:
  • Aleksey S. Polunchenko

    (University of Southern California)

  • Alexander G. Tartakovsky

    (University of Southern California)

Abstract

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.

Suggested Citation

  • Aleksey S. Polunchenko & Alexander G. Tartakovsky, 2012. "State-of-the-Art in Sequential Change-Point Detection," Methodology and Computing in Applied Probability, Springer, vol. 14(3), pages 649-684, September.
    • Handle: RePEc:spr:metcap:v:14:y:2012:i:3:d:10.1007_s11009-011-9256-5
    DOI: 10.1007/s11009-011-9256-5
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    Cited by:

    1. Krawiec, Michał & Palmowski, Zbigniew & Płociniczak, Łukasz, 2018. "Quickest drift change detection in Lévy-type force of mortality model," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 432-450.
    2. Michal Shauly-Aharonov & Orit Barenholz-Goultschin, 2019. "Real-Time Change-Point Detection Algorithm with an Application to Glycemic Control for Diabetic Pregnant Women," Methodology and Computing in Applied Probability, Springer, vol. 21(3), pages 931-944, September.
    3. Aleksey S. Polunchenko & Grigory Sokolov, 2016. "An Analytic Expression for the Distribution of the Generalized Shiryaev–Roberts Diffusion," Methodology and Computing in Applied Probability, Springer, vol. 18(4), pages 1153-1195, December.
    4. Savas Dayanik & Semih O Sezer, 2023. "Model Misspecification in Discrete Time Bayesian Online Change Detection," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-27, March.

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