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Compound sequential change-point detection in parallel data streams

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  • Chen, Yunxiao
  • Li, Xiaoou

Abstract

We consider sequential change-point detection in parallel data streams, where each stream has its own change point. Once a change is detected in a data stream, this stream is deactivated permanently. The goal is to maximize the normal operation of the pre-change streams, while controlling the proportion of post-change streams among the active streams at all time points. Taking a Bayesian formulation, we develop a compound decision framework for this problem. A procedure is proposed that is uniformly optimal among all sequential procedures which control the expected proportion of post-change streams at all time points. We also investigate the asymptotic behavior of the proposed method when the number of data streams grows large. Numerical examples are provided to illustrate the use and performance of the proposed method.

Suggested Citation

  • Chen, Yunxiao & Li, Xiaoou, 2023. "Compound sequential change-point detection in parallel data streams," LSE Research Online Documents on Economics 111010, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:111010
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    References listed on IDEAS

    as
    1. Wim Linden & Charles Lewis, 2015. "Bayesian Checks on Cheating on Tests," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 689-706, September.
    2. Sun, Wenguang & Cai, T. Tony, 2007. "Oracle and Adaptive Compound Decision Rules for False Discovery Rate Control," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 901-912, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    sequential analysis; change-point detection; compound decision; false non-discovery rate; large-scale inference;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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