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Efficient monitoring of autocorrelated Poisson counts

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  • Jian Li
  • Qiang Zhou
  • Dong Ding

Abstract

Statistical surveillance for autocorrelated Poisson counts has drawn considerable attention recently. These works are usually based on a first-order integer-valued autoregressive model and focus on monitoring separately either the marginal mean or the autocorrelation coefficient. Inspired by multivariate statistical process control, this article transforms autocorrelated Poisson counts into a bivariate representation and proposes an efficient control chart. By borrowing the power of the likelihood ratio test, albeit surprisingly, this chart demonstrates almost uniformly stronger power than the existing alternatives in simultaneously detecting shifts in both the marginal mean and the autocorrelation coefficient. In addition, the robustness of the proposed chart against overdispersion encountered often in counts is also verified. It is shown that this chart also has superiority in monitoring autocorrelated overdispersed counts.

Suggested Citation

  • Jian Li & Qiang Zhou & Dong Ding, 2020. "Efficient monitoring of autocorrelated Poisson counts," IISE Transactions, Taylor & Francis Journals, vol. 52(7), pages 769-779, July.
    • Handle: RePEc:taf:uiiexx:v:52:y:2020:i:7:p:769-779
    DOI: 10.1080/24725854.2019.1649506
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    Cited by:

    1. Albu, Nadia & Albu, Cătălin Nicolae & Cho, Charles H. & Pesci, Caterina, 2023. "Not on the ruins, but with the ruins of the past – Inertia and change in the financial reporting field in a transitioning country," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 96(C).

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