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An accurate evaluation of adaptive exponentially weighted moving average schemes

Author

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  • Wenpo Huang
  • Lianjie Shu
  • Yan Su

Abstract

As a natural generalization of the conventional Exponentially Weighted Moving Average (EWMA) monitoring scheme, the Adaptive EWMA (AEWMA) scheme has received a great deal of attention. The Markov chain method was originally used to approximate the average run length performance of the AEWMA chart; however, this method may suffer from the issue of slow convergence and unstable approximation due to kernel discontinuity. In order to overcome this issue, this article extends the piecewise collocation method and the Clenshaw–Curtis (CC) quadrature (method) to the evaluation of AEWMA chart performance. It is shown that both the collocation and CC quadrature methods are very competitive and can provide more accurate and fast approximation to the run length performance of AEWMA charts than the conventional Markov chain approach.

Suggested Citation

  • Wenpo Huang & Lianjie Shu & Yan Su, 2014. "An accurate evaluation of adaptive exponentially weighted moving average schemes," IISE Transactions, Taylor & Francis Journals, vol. 46(5), pages 457-469.
    • Handle: RePEc:taf:uiiexx:v:46:y:2014:i:5:p:457-469
    DOI: 10.1080/0740817X.2013.803642
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    Cited by:

    1. Mitra, Amitava & Lee, Kang Bok & Chakraborti, Subhabrata, 2019. "An adaptive exponentially weighted moving average-type control chart to monitor the process mean," European Journal of Operational Research, Elsevier, vol. 279(3), pages 902-911.
    2. Amir Ahmadi-Javid & Mohsen Ebadi, 2021. "Economic design of memory-type control charts: The fallacy of the formula proposed by Lorenzen and Vance (1986)," Computational Statistics, Springer, vol. 36(1), pages 661-690, March.

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