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

Aleksey S. Polunchenko () and Alexander G. Tartakovsky ()
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Aleksey S. Polunchenko: University of Southern California
Alexander G. Tartakovsky: University of Southern California

Methodology and Computing in Applied Probability, 2012, vol. 14, issue 3, 649-684

Abstract: 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.

Keywords: 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; 62L10; 60G40; 62C10; 62C20 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11009-011-9256-5

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