Non-parametric quickest detection of a change in the mean of an observation sequence

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We study the problem of quickest detection of a change in the mean of an observation sequence, under the assumption that both the pre- and post-change distributions have bounded support. We first study the case where the pre-change distribution is known, and then study the extension where only the mean and variance of the pre-change distribution are known. In both cases, no knowledge of the post-change distribution is assumed other than that it has bounded support. For the case where the pre-change distribution is known, we derive a test that asymptotically minimizes the worst-case detection delay over all post-change distributions, as the false alarm rate goes to zero. We then study the limiting form of the optimal test as the gap between the pre- and post-change means goes to zero, which we call the Mean-Change Test (MCT). We show that the MCT can be designed with only knowledge of the mean and variance of the pre-change distribution. We validate our analysis through numerical results for detecting a change in the mean of a beta distribution. We also demonstrate the use of the MCT for pandemic monitoring.

Original languageEnglish (US)
Title of host publication2021 55th Annual Conference on Information Sciences and Systems, CISS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665412681
DOIs
StatePublished - Mar 24 2021
Event55th Annual Conference on Information Sciences and Systems, CISS 2021 - Baltimore, United States
Duration: Mar 24 2021Mar 26 2021

Publication series

Name2021 55th Annual Conference on Information Sciences and Systems, CISS 2021

Conference

Conference55th Annual Conference on Information Sciences and Systems, CISS 2021
CountryUnited States
CityBaltimore
Period3/24/213/26/21

Keywords

  • Minimax robust detection
  • Nonparametric methods
  • Quickest change detection (QCD)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management

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