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Monitoring the covariance matrix via penalized likelihood estimation

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  • Bo Li
  • Kaibo Wang
  • Arthur Yeh

Abstract

In many industrial multivariate quality control applications, based on the engineering and operational understanding of how the process works, when the process variability is out of control it is typically the case that changes only occur in a small number of elements in the covariance matrix. Under such a premise, we propose a new Phase II Shewhart chart for monitoring changes in the covariance matrix of a multivariate normal process. The new control chart is essentially based on calculating the likelihood ratio of testing the hypothesis that the in-control covariance matrix is equal to a known covariance matrix, where the unknown covariance matrix that appears in the likelihood ratio is replaced by an estimate obtained from a penalized likelihood function. The penalized likelihood function is derived by adding an L1 penalty function to the usual likelihood. The performance of the proposed chart is evaluated based on simulations and compared with that of several existing Shewhart charts for monitoring the covariance matrix. The simulation results indicate that the proposed chart outperforms existing charts. A real example from the semiconductor industry is presented and analyzed using the proposed chart and other existing charts. Potential future research directions are also discussed.

Suggested Citation

  • 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.
  • Handle: RePEc:taf:uiiexx:v:45:y:2013:i:2:p:132-146
    DOI: 10.1080/0740817X.2012.663952
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    Cited by:

    1. Sangahn Kim & Mehmet Turkoz & Myong K. Jeong & Elsayed A. Elsayed, 2024. "Monitoring of group-structured high-dimensional processes via sparse group LASSO," Annals of Operations Research, Springer, vol. 340(2), pages 891-911, September.
    2. 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.
    3. 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.

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