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A new multivariate variability control chart based on a covariance matrix combination

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  • Jose Luis Alfaro
  • Juan Fco. Ortega

Abstract

In the field of multivariate quality control, there are many control charts related to the process mean but few options addressing process variability. Variability control charts have two main drawbacks: the first relates to the number of parameters to tune and the second relates to how changes in the mean affect the performance of these charts. Thus, in this paper, we propose a new multivariate variability control chart, called the multivariate exponentially weighted covariance matrix combination, which solves these two problems. The results show that this new chart performs well in the detection of changes in variance when the mean does not change and outperforms other charts when the mean does change.

Suggested Citation

  • Jose Luis Alfaro & Juan Fco. Ortega, 2019. "A new multivariate variability control chart based on a covariance matrix combination," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(3), pages 823-836, May.
    • Handle: RePEc:wly:apsmbi:v:35:y:2019:i:3:p:823-836
    DOI: 10.1002/asmb.2412
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    References listed on IDEAS

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    1. Maravelakis, Petros E. & Castagliola, Philippe, 2009. "An EWMA chart for monitoring the process standard deviation when parameters are estimated," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2653-2664, May.
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