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Multivariate EWMA control chart based on a variable selection using AIC for multivariate statistical process monitoring

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  • Nishimura, Kazuya
  • Matsuura, Shun
  • Suzuki, Hideo

Abstract

In multivariate statistical process control, when a process shift occurs, not all variables but a few variables may shift from the in-control state. This paper proposes a multivariate EWMA control chart based on a variable selection using AIC.

Suggested Citation

  • Nishimura, Kazuya & Matsuura, Shun & Suzuki, Hideo, 2015. "Multivariate EWMA control chart based on a variable selection using AIC for multivariate statistical process monitoring," Statistics & Probability Letters, Elsevier, vol. 104(C), pages 7-13.
    • Handle: RePEc:eee:stapro:v:104:y:2015:i:c:p:7-13
    DOI: 10.1016/j.spl.2015.05.003
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    References listed on IDEAS

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    1. Bo Li & Kaibo Wang & Arthur Yeh, 2013. "Monitoring the covariance matrix via penalized likelihood estimation," IISE Transactions, Taylor & Francis Journals, vol. 45(2), pages 132-146.
    2. Zou, Changliang & Qiu, Peihua, 2009. "Multivariate Statistical Process Control Using LASSO," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1586-1596.
    3. Wang, Kaibo & Yeh, Arthur B. & Li, Bo, 2014. "Simultaneous monitoring of process mean vector and covariance matrix via penalized likelihood estimation," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 206-217.
    4. Bersimis, Sotiris & Psarakis, Stelios & Panaretos, John, 2006. "Multivariate Statistical Process Control Charts: An Overview," MPRA Paper 6399, University Library of Munich, Germany.
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