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Original Articles

Monitoring multivariate process variability with individual observations via penalised likelihood estimation

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Pages 6624-6638 | Received 05 Oct 2011, Accepted 09 Mar 2012, Published online: 27 Apr 2012

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Nurudeen A. Adegoke, Jimoh Olawale Ajadi, Amitava Mukherjee & Saddam Akber Abbasi. (2022) Nonparametric multivariate covariance chart for monitoring individual observations. Computers & Industrial Engineering 167, pages 108025.
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Jimoh Olawale Ajadi, Angus Wong, Tahir Mahmood & Kevin Hung. (2021) A new multivariate CUSUM chart for monitoring of covariance matrix with individual observations under estimated parameter. Quality and Reliability Engineering International 38:2, pages 834-847.
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Humberto Nuno Teixeira, Isabel Lopes, Ana Cristina Braga, Pedro Delgado & Cristina Martins. 2022. Innovations in Mechatronics Engineering. Innovations in Mechatronics Engineering 1 13 .
Edgard M. Maboudou-Tchao. (2021) Monitoring the mean with least-squares support vector data description. Gestão & Produção 28:3.
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Galal M. Abdella, Mohammad Reza Maleki, Sangahn Kim, Khalifa N. Al-Khalifa & Abdel Magid S. Hamouda. (2020) Phase-I monitoring of high-dimensional covariance matrix using an adaptive thresholding LASSO rule. Computers & Industrial Engineering 144, pages 106465.
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Xiu Ning & Pingke Li. (2019) A simulation comparison of some distance‐based EWMA control charts for monitoring the covariance matrix with individual observations. Quality and Reliability Engineering International 36:1, pages 50-67.
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Xiaoxiao Guo, Zhen He & Hui Chen. (2019) A Real-Time Contrasts Method for Monitoring Image Data. A Real-Time Contrasts Method for Monitoring Image Data.
Jinho Kim, Galal M. Abdella, Sangahn Kim, Khalifa N. Al-Khalifa & Abdel Magid Hamouda. (2019) Control charts for variability monitoring in high-dimensional processes. Computers & Industrial Engineering 130, pages 309-316.
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Zhonghua Li & Fugee Tsung. (2018) A control scheme for monitoring process covariance matrices with more variables than observations. Quality and Reliability Engineering International 35:1, pages 351-367.
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Patrice Lajoie, Jonathan Gaudreault, Nadia Lehoux & Maha Ben Ali. (2019) A data-driven framework to deal with intrinsic variability of industrial processes: An application in the textile industry. IFAC-PapersOnLine 52:13, pages 731-736.
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Wei-Heng Huang & Arthur B. Yeh. (2018) A Nonparametric Phase I Control Chart for Monitoring the Process Variability with Individual Observations Based on Empirical Likelihood Ratio. International Journal of Reliability, Quality and Safety Engineering 25:03, pages 1850015.
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Jinyu Fan, Lianjie Shu, Honghao Zhao & Hangfai Yeung. (2017) Monitoring multivariate process variability via eigenvalues. Computers & Industrial Engineering 113, pages 269-281.
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Kazuya Nishimura, Shun Matsuura & Hideo Suzuki. (2015) Multivariate EWMA control chart based on a variable selection using AIC for multivariate statistical process monitoring. Statistics & Probability Letters 104, pages 7-13.
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Letícia Pereira Pinto & Sueli Aparecida Mingoti. (2015) ON HYPOTHESIS TESTS FOR COVARIANCE MATRICES UNDER MULTIVARIATE NORMALITY. Pesquisa Operacional 35:1, pages 123-142.
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Ching‐Ren Cheng & Jyh‐Jen Horng Shiau. (2014) A Distribution‐Free Multivariate Control Chart for Phase I Applications. Quality and Reliability Engineering International 31:1, pages 97-111.
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Kaibo Wang, Arthur B. Yeh & Bo Li. (2014) Simultaneous monitoring of process mean vector and covariance matrix via penalized likelihood estimation. Computational Statistics & Data Analysis 78, pages 206-217.
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Yanfen Shang, Xuemin Zi, Fugee Tsung & Zhen He. (2014) LASSO-based diagnosis scheme for multistage processes with binary data. Computers & Industrial Engineering 72, pages 198-205.
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