Teoriya Veroyatnostei i ee Primeneniya
General information
Latest issue
Impact factor
Guidelines for authors
Submit a manuscript

Search papers
Search references

Latest issue
Current issues
Archive issues
What is RSS

Teor. Veroyatnost. i Primenen.:

Personal entry:
Save password
Forgotten password?

Teor. Veroyatnost. i Primenen., 2008, Volume 53, Issue 3, Pages 472–499 (Mi tvp2443)  

This article is cited in 8 scientific papers (total in 8 papers)

Asymptotic Optimality in Bayesian Changepoint Detection Problems under Global False Alarm Probability Constraint

A. G. Tartakovskii

University of Southern California

Abstract: In the 1960s Shiryaev developed the Bayesian theory of changepoint detection in independent and identically distributed (i.i.d.) sequences. In Shiryaev's classical setting the goal is to minimize an average delay to detection under the constraint imposed on the average probability of false alarm. Recently, Tartakovsky and Veeravalli [Theory Probab. Appl., 49 (2005), pp. 458–497] developed a general Bayesian asymptotic changepoint detection theory (in the classical setting) that is not limited to a restrictive i.i.d. assumption. It was proved that Shiryaev's detection procedure is asymptotically optimal under traditional average false alarm probability constraint, assuming that this probability is small. In the present paper, we consider a less conventional approach where the constraint is imposed on the global, supremum false alarm probability. An asymptotically optimal Bayesian change detection procedure is proposed and thoroughly evaluated for both i.i.d. and non-i.i.d. models when the global false alarm probability approaches zero.

Keywords: asymptotic optimality, changepoint detection, cumulative sum procedure, global false alarm probability, nonlinear renewal theory, Shiryaev's rule, sequential detection.

DOI: https://doi.org/10.4213/tvp2443

Full text: PDF file (2726 kB)
References: PDF file   HTML file

English version:
Theory of Probability and its Applications, 2009, 53:3, 443–466

Bibliographic databases:

Received: 11.08.2006

Citation: A. G. Tartakovskii, “Asymptotic Optimality in Bayesian Changepoint Detection Problems under Global False Alarm Probability Constraint”, Teor. Veroyatnost. i Primenen., 53:3 (2008), 472–499; Theory Probab. Appl., 53:3 (2009), 443–466

Citation in format AMSBIB
\by A.~G.~Tartakovskii
\paper Asymptotic Optimality in Bayesian Changepoint Detection Problems under Global False Alarm Probability Constraint
\jour Teor. Veroyatnost. i Primenen.
\yr 2008
\vol 53
\issue 3
\pages 472--499
\jour Theory Probab. Appl.
\yr 2009
\vol 53
\issue 3
\pages 443--466

Linking options:
  • http://mi.mathnet.ru/eng/tvp2443
  • https://doi.org/10.4213/tvp2443
  • http://mi.mathnet.ru/eng/tvp/v53/i3/p472

    SHARE: VKontakte.ru FaceBook Twitter Mail.ru Livejournal Memori.ru

    Citing articles on Google Scholar: Russian citations, English citations
    Related articles on Google Scholar: Russian articles, English articles

    This publication is cited in the following articles:
    1. Verdier G., Hilgert N., Vila J.-P., “Optimality of CUSUM rule approximations in change-point detection problems: application to nonlinear state-space systems”, IEEE Trans. Inform. Theory, 54:11 (2008), 5102–5112  crossref  mathscinet  zmath  isi  scopus
    2. Polunchenko A.S. Tartakovsky A.G., “State-of-the-art in sequential change-point detection”, Methodol. Comput. Appl. Probab., 14:3 (2012), 649–684  crossref  mathscinet  zmath  isi  elib  scopus
    3. Chen J., Zhang W., Poor H.V., “On Parallel Sequential Change Detection Controlling False Discovery Rate”, 2016 50Th Asilomar Conference on Signals, Systems and Computers, Conference Record of the Asilomar Conference on Signals Systems and Computers, ed. Matthews M., IEEE Computer Soc, 2016, 107–111  isi
    4. Chronopoulou A., Nagi R., “Online Community Detection For Fused Social Network Graphs”, 2016 19Th International Conference on Information Fusion (Fusion), IEEE, 2016, 1682–1686  isi
    5. Han D. Tsung F. Xian J., “On the Optimality of Bayesian Change-Point Detection”, Ann. Stat., 45:4 (2017), 1375–1402  crossref  mathscinet  zmath  isi  scopus
    6. Nitzan E. Halme T. Koivunen V., “Bayesian Methods For Multiple Change-Point Detection With Reduced Communication”, IEEE Trans. Signal Process., 68 (2020), 4871–4886  crossref  mathscinet  isi
    7. Ford J.J. James J. Molloy T.L., “On the Informativeness of Measurements in Shiryaev'S Bayesian Quickest Change Detection”, Automatica, 111 (2020), 108645  crossref  mathscinet  isi
    8. Halme T., Nitzan E., Poor H.V., Koivunen V., “Bayesian Multiple Change-Point Detection With Limited Communication”, 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, International Conference on Acoustics Speech and Signal Processing Icassp, IEEE, 2020, 5490–5494  isi
  • Теория вероятностей и ее применения Theory of Probability and its Applications
    Number of views:
    This page:331
    Full text:143

    Contact us:
     Terms of Use  Registration to the website  Logotypes © Steklov Mathematical Institute RAS, 2021