References
- Ahmad, S., M. Riaz, S. A. Abbasi, and Z. Lin. 2014. On median control charting under double sampling scheme. European Journal of Industrial Engineering 8 (4):478–512. doi: https://doi.org/10.1504/EJIE.2014.064755.
- Ahmad, S., M. Riaz, S. A. Abbasi, and Z. Y. Lin. 2014. On efficient median control charting. Journal of the Chinese Institute of Engineers 37 (3):358–75. doi: https://doi.org/10.1080/02533839.2013.781794.
- Ahmad, S., S. A. Abbasi, M. Riaz, and N. Abbas. 2014. On efficient use of auxiliary information for control charting in SPC. Computers & Industrial Engineering 67:173–84. doi: https://doi.org/10.1016/j.cie.2013.11.004.
- Benneyan, J. C., R. C. Lloyd, and P. E. Plsek. 2003. Statistical process control as a tool for research and healthcare improvement. Quality and Safety in Health Care 12 (6):458–64. doi: https://doi.org/10.1136/qhc.12.6.458.
- Blake, C. L., and C. J. Merz. 1998. UCI Repository of Machine Learning Databases http://www.ics.uci.edu/mlearn/MLRepository.html.
- Capizzi, G., and G. Masarotto. 2011. A least angle regression control chart for multidimensional data. Technometrics 53 (3):285–96. doi: https://doi.org/10.1198/TECH.2011.10027.
- Cai, D., X. He, J. Wen, J. Han, and W. Ma. 2006. Support Tensor Machines for Text Categorization, Computer Science Department. Technical report. UIUCDCS–R–200–(2714)
- Chen, Y., K. Wang, and P. Zhong. 2016. One-class support tensor machine. Knowledge-Based Systems 96:14–28. doi: https://doi.org/10.1016/j.knosys.2016.01.007.
- Elbasi, E., L. Zuo, K. Mehrota, C. Mohan, and P. Varshney. 2005. Control charts approach for scenario recognition in video sequences. Turkish Journal of Electrical Engineering and Computer Sciences 13:303–9.
- Gorman, R. P., and T. J. Sejnowski. 1988. Analysis of hidden units in a layered network trained to classify sonar targets. Neural Networks 1 (1):75–89. doi: https://doi.org/10.1016/0893-6080(88)90023-8.
- Kampmann, G., and O. Nelles. 2014. One-class LS-SVM with zero leave-one-out error”, 2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA), Orlando, FL, USA.
- Kumar, S., A. K. Choudhary, M. Kumar, R. Shankar, and M. K. Tiwari. 2006. Kernel distance-based robust support vector methods and its application in developing a robust K-chart. International Journal of Production Research 44 (1):77–96. doi: https://doi.org/10.1080/00207540500216037.
- Li, B., K. Wang, and A. B. Yeh. 2013. Monitoring the covariance matrix via penalized likelihood estimation. IIE Transactions 45 (2):132–46. doi: https://doi.org/10.1080/0740817X.2012.663952.
- Maboudou-Tchao, E. M., and V. Agboto. 2013. Monitoring the covariance matrix with fewer observations than variables. Computational Statistics and Data Analysis 64:99–112. doi: https://doi.org/10.1016/j.csda.2013.02.028.
- Maboudou-Tchao, E. M., and N. Diawara. 2013. A lasso chart for monitoring the covariance matrix. Quality Technology and Quantitative Management 10 (1):95–114. doi: https://doi.org/10.1080/16843703.2013.11673310.
- Maboudou-Tchao, E. M., and I. Silva. 2013. Tests for mean vectors in high dimension. Statistical Analysis and Data Mining 6 (6):578–98. doi: https://doi.org/10.1002/sam.11209.
- Maboudou-Tchao, E. M., I. Silva, and N. Diawara. 2016. Monitoring the mean vector with Mahalanobis kernels. Quality Technology and Quantitative Management (QTQM) 15 (4), 459–474. doi: https://doi.org/10.1080/16843703.2016.1226707
- Maboudou-Tchao, E. M. 2017. Kernel methods for changes detection in covariance matrices. Communications in Statistics - Simulation and Computation 47 (6), 1704–1721. https://doi.org/http://dx.doi.org/10.1080/03610918.2017.1322701
- Park, Y. 2005. “A Statistical process control approach for network intrusion detection”, Doctoral dissertation, Georgia Institute of Technology.
- Silva, I., E. M. Maboudou-Tchao, and W. L. de Figueiredo. 2018. Frequentist-Bayesian Monte Carlo test for mean vectors in high dimension. Journal of Computational Applied Mathematics 333:51–64.
- Sun, R., Tsung. F., and A. 2003. Kernel-distance-based multivariate control charts using support vector methods. International Journal of Production Research 41 (13):2975–89. doi: https://doi.org/10.1080/1352816031000075224.
- Tax, D., and R. Duin. 1999. Support vector domain description. Pattern Recognition Letters 20 (11–13):1191–9. doi: https://doi.org/10.1016/S0167-8655(99)00087-2.
- Wang, K., and W. Jiang. 2009. High-dimensional process monitoring and fault isolation via variable selection. Journal of Quality Technology 41 (3):247–58. doi: https://doi.org/10.1080/00224065.2009.11917780.
- Wang, K., A. B. Yeh, and B. Li. 2014. Simultaneous monitoring of process mean vector and covariance matrix via penalized likelihood estimation. Computational Statistics and Data Analysis 78:206–17. doi: https://doi.org/10.1016/j.csda.2014.04.017.
- Wu, Z., J. X. Jiao, M. Yang, Y. Liu, and Z. Wang. 2009. An enhanced adaptive Cusum control chart. IIE Transactions 41 (7):642–53. doi: https://doi.org/10.1080/07408170802712582.
- Yeh, A. B., B. Li, and K. Wang. 2012. Monitoring multivariate process variability with individual observations via penalized likelihood estimation. International Journal of Production Research 50 (22):6624–38. doi: https://doi.org/10.1080/00207543.2012.676684.
- Zou, C., W. Jiang, and F. Tsung. 2011. A lasso-based diagnostic framework for multivariate statistical process control. Technometrics 53 (3):297–309. doi: https://doi.org/10.1198/TECH.2011.10034.