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The double sampling and the EWMA charts based on the sample variances

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  • Machado, Marcela A.G.
  • Costa, Antonio F.B.

Abstract

We propose a new statistic to control the covariance matrix of bivariate processes. This new statistic is based on the sample variances of the two quality characteristics, in short VMAX statistic. The points plotted on the chart correspond to the maximum of the values of these two variances. The reasons to consider the VMAX statistic instead of the generalized variance |S| is its faster detection of process changes and its better diagnostic feature; that is, with the VMAX statistic it is easier to identify the out-of-control variable. We study the double sampling (DS) and the exponentially weighted moving average (EWMA) charts based on the VMAX statistic.

Suggested Citation

  • Machado, Marcela A.G. & Costa, Antonio F.B., 2008. "The double sampling and the EWMA charts based on the sample variances," International Journal of Production Economics, Elsevier, vol. 114(1), pages 134-148, July.
    • Handle: RePEc:eee:proeco:v:114:y:2008:i:1:p:134-148
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    References listed on IDEAS

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    1. Arthur Yeh & Dennis Lin & Honghong Zhou & Chandramouliswaran Venkataramani, 2003. "A multivariate exponentially weighted moving average control chart for monitoring process variability," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(5), pages 507-536.
    2. Costa, A.F.B. & Rahim, M.A., 2006. "The non-central chi-square chart with two-stage samplings," European Journal of Operational Research, Elsevier, vol. 171(1), pages 64-73, May.
    3. He, David & Grigoryan, Arsen, 2006. "Joint statistical design of double sampling and s charts," European Journal of Operational Research, Elsevier, vol. 168(1), pages 122-142, January.
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    Cited by:

    1. Peruchi, Rogério Santana & Balestrassi, Pedro Paulo & de Paiva, Anderson Paulo & Ferreira, João Roberto & de Santana Carmelossi, Michele, 2013. "A new multivariate gage R&R method for correlated characteristics," International Journal of Production Economics, Elsevier, vol. 144(1), pages 301-315.
    2. Wu, Zhang & Jiao, Jianxin & He, Zhen, 2009. "A single control chart for monitoring the frequency and magnitude of an event," International Journal of Production Economics, Elsevier, vol. 119(1), pages 24-33, May.
    3. Ou, Yanjing & Wu, Zhang & Goh, Thong Ngee, 2011. "A new SPRT chart for monitoring process mean and variance," International Journal of Production Economics, Elsevier, vol. 132(2), pages 303-314, August.
    4. Huwang, Longcheen & Huang, Chun-Jung & Wang, Yi-Hua Tina, 2010. "New EWMA control charts for monitoring process dispersion," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2328-2342, October.
    5. Wu, Zhang & Khoo, Michael B.C. & Shu, Lianjie & Jiang, Wei, 2009. "An np control chart for monitoring the mean of a variable based on an attribute inspection," International Journal of Production Economics, Elsevier, vol. 121(1), pages 141-147, September.
    6. A. F.B. Costa & M. A.G. Machado, 2011. "A control chart based on sample ranges for monitoring the covariance matrix of the multivariate processes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(2), pages 233-245, September.
    7. Ahmad, Shabbir & Riaz, Muhammad & Abbasi, Saddam Akber & Lin, Zhengyan, 2013. "On monitoring process variability under double sampling scheme," International Journal of Production Economics, Elsevier, vol. 142(2), pages 388-400.
    8. Costa, Antonio Fernando Branco & Machado, Marcela Aparecida Guerreiro, 2011. "Variable parameter and double sampling charts in the presence of correlation: The Markov chain approach," International Journal of Production Economics, Elsevier, vol. 130(2), pages 224-229, April.

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