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Monitoring Autocorrelated Process Mean And Variance Using A Gwma Chart Based On Residuals

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Author Info

  • SHEY-HUEI SHEU

    ()
    (Department of Industrial Management, National Taiwan University of Science and Technology, 43, Keelung Road, Section 4, Taipei 106, Taiwan)

  • SHIN-LI LU

    (Department of Industrial Management and Enterprise Information, Aletheia University, 32, Chen-Li Street, Tamsui, Taipei 251, Taiwan)

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    Abstract

    This investigation elucidates the feasibility of monitoring a process for which observational data are largely autocorrelated. Special causes typically affect not only the process mean but also the process variance. The EWMA control chart has recently been developed and adopted to detect small shifts in the process mean and/or variance. This work extends the EWMA control chart, called the generally weighted moving average (GWMA) control chart, to monitor a process in which the observations can be regarded as a first-order autoregressive process with a random error. The EWMA and GWMA control charts of residuals used to monitor process variability and to monitor simultaneously the process mean and variance are considered to evaluate how average run lengths (ARLs) differ in each case.

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    Bibliographic Info

    Article provided by World Scientific Publishing Co. Pte. Ltd. in its journal Asia-Pacific Journal of Operational Research.

    Volume (Year): 25 (2008)
    Issue (Month): 06 ()
    Pages: 781-792

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    Handle: RePEc:wsi:apjorx:v:25:y:2008:i:06:p:781-792

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    Related research

    Keywords: Autocorrelation; EWMA; generally weighted moving average; autoregressive process; average run length;

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