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An adaptive CUSUM chart for monitoring poisson rates with increasing population sizes

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  • Honghao Zhao
  • Lianjie Shu
  • Wei Jiang
  • Kwok-Leung Tsui

Abstract

The CUSUM control chart is an effective tool for monitoring both variable and count data. However, it often requires the assumption of known process parameters. To get away from this assumption, the adaptive CUSUM (ACUSUM) procedure has been suggested, with primary focus on the monitoring of variable data. This paper extends the idea of ACUSUM control charts from the monitoring of variable data to the monitoring of count data based on an inhomogeneous Poisson model. The study is motivated by a real example of male thyroid cancer data in New Mexico. The comparison results show that the ACUSUM method generally performs better than the traditional Poisson CUSUM charts and other variations for monitoring a wide range of shifts in the Poisson mean. [Received 11 January 2014; Revised 15 July 2014; Accepted 25 August 2014]

Suggested Citation

  • Honghao Zhao & Lianjie Shu & Wei Jiang & Kwok-Leung Tsui, 2015. "An adaptive CUSUM chart for monitoring poisson rates with increasing population sizes," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 9(5), pages 692-715.
    • Handle: RePEc:ids:eujine:v:9:y:2015:i:5:p:692-715
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    Cited by:

    1. Honghao Zhao & Huajun Tang & Chuan Pang & Huimin Jiang, 2022. "A Markov Chain Model for Approximating the Run Length Distributions of Poisson EWMA Charts under Linear Drifts," Mathematics, MDPI, vol. 10(24), pages 1-12, December.

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