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Original Articles

Contributors to a multivariate statistical process control chart signal

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Pages 2203-2213 | Received 01 Jan 1996, Published online: 27 Jun 2007

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MatthiasH. Y. Tan & Jianjun Shi. (2012) A Bayesian Approach for Interpreting Mean Shifts in Multivariate Quality Control. Technometrics 54:3, pages 294-307.
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Francisco Aparisi, Marco A. de Luna & Eugenio Epprecht. (2010) Optimisation of a set of or principal components control charts using genetic algorithms. International Journal of Production Research 48:18, pages 5345-5361.
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ShingI. Chang & Shih-Hsiung Chou. (2010) A Visualization Decision Support Tool for Multivariate SPC Diagnosis Using Marginal CUSUM Glyphs. Quality Engineering 22:3, pages 182-198.
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H. Brian Hwarng & Yu Wang. (2010) Shift detection and source identification in multivariate autocorrelated processes. International Journal of Production Research 48:3, pages 835-859.
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E. Alfaro, J.L. Alfaro, M. Gámez & N. García. (2009) A boosting approach for understanding out-of-control signals in multivariate control charts. International Journal of Production Research 47:24, pages 6821-6834.
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Paul Zantek, Shan Li & Yong Chen. (2007) Detecting multiple special causes from multivariate data with applications to fault detection in manufacturing. IIE Transactions 39:8, pages 771-782.
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Shing I. Chang & Kui Zhang. (2007) Statistical Process Control for Variance Shift Detections of Multivariate Autocorrelated Processes. Quality Technology & Quantitative Management 4:3, pages 413-435.
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Francisco Aparisi, Gerardo Avendaño & JosÉ Sanz. (2006) Techniques to interpret T 2 control chart signals. IIE Transactions 38:8, pages 647-657.
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Murat Caner Testik & GeorgeC. Runger. (2006) Multivariate one-sided control charts. IIE Transactions 38:8, pages 635-645.
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Julie Badcock, Trevor C Bailey, Philip Jonathan & Wojtek J Krzanowski. (2004) Two Projection Methods for Use in the Analysis of Multivariate Process Data With an Illustration in Petrochemical Production. Technometrics 46:4, pages 392-403.
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P. E. Maravelakis, S. Bersimis, J. Panaretos & S. Psarakis. (2002) IDENTIFYING THE OUT OF CONTROL VARIABLE IN A MULTIVARIATE CONTROL CHART. Communications in Statistics - Theory and Methods 31:12, pages 2391-2408.
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Robert L. Mason & John C. Young. (1999) Improving the Sensitivity of the T2 Statistic in Multivariate Process Control. Journal of Quality Technology 31:2, pages 155-165.
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. (2002) Monitoring process manufacturing performance. IEEE Control Systems 22:5, pages 26-39.
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Frank Alt & Kamlesh Jain. 2001. Encyclopedia of Operations Research and Management Science. Encyclopedia of Operations Research and Management Science 544 550 .
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