Asymptotically Optimal Quickest Change Detection in Multistream Data—Part 1: General Stochastic Models

  • Alexander G. TartakovskyEmail author


Assume that there are multiple data streams (channels, sensors) and in each stream the process of interest produces generally dependent and non-identically distributed observations. When the process is in a normal mode (in-control), the (pre-change) distribution is known, but when the process becomes abnormal there is a parametric uncertainty, i.e., the post-change (out-of-control) distribution is known only partially up to a parameter. Both the change point and the post-change parameter are unknown. Moreover, the change affects an unknown subset of streams, so that the number of affected streams and their location are unknown in advance. A good changepoint detection procedure should detect the change as soon as possible after its occurrence while controlling for a risk of false alarms. We consider a Bayesian setup with a given prior distribution of the change point and propose two sequential mixture-based change detection rules, one mixes a Shiryaev-type statistic over both the unknown subset of affected streams and the unknown post-change parameter and another mixes a Shiryaev–Roberts-type statistic. These rules generalize the mixture detection procedures studied by Tartakovsky (IEEE Trans Inf Theory 65(3):1413–1429, 2019) in a single-stream case. We provide sufficient conditions under which the proposed multistream change detection procedures are first-order asymptotically optimal with respect to moments of the delay to detection as the probability of false alarm approaches zero.


Asymptotic optimality Changepoint detection General non-i.i.d. models Hidden Markov models Moments of the delay to detection r-Complete convergence Statistical process control Surveillance 

Mathematics Subject Classification (2010)

MSC 62L10 MSC 62L15 MSC 60G40 MSC 62C10 MSC 62C20 


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The work was supported in part by the Russian Ministry of Education and Science 5-100 excellence project, the Russian Federation Ministry of Education and Science Arctic program and the grant 18-19-00452 from the Russian Science Foundation at the Moscow Institute of Physics and Technology.

The author would like to thank two referees for useful comments.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Space Informatics LaboratoryMoscow Institute of Physics and TechnologyDolgoprudnyRussia
  2. 2.AGT StatConsultLos AngelesUSA

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