Monitoring location and scale of multivariate processes subject to a multiplicity of assignable causes

References

• Alt, F. B. (1985). Multivariate quality control. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences (pp. 110–122). New York, NY: Wiley.
   Google Scholar
• Alt, F. B., & Bedewi, G. E. (1986). SPC for dispersion for multivariate data. ASQC Quality Congress Transactions, 248–254.
   Google Scholar
• Aparisi, F. (1996). Hotelling’s T2 control chart with adaptive sample sizes. International Journal of Production Research, 34(10), 2853–2862.
   Web of Science ®Google Scholar
• Aparisi, F., & Haro, C. L. (2003). A comparison of T2 control charts with variable sampling schemes as opposed to MEWMA chart. International Journal of Production Research, 41(10), 2169–2182.
   Web of Science ®Google Scholar
• Aparisi, F., Jabaloyes, J., & Carrión, A. (1999). Statistical properties of the S multivariate control chart. Communications in Statistics- Theory and Methods, 28(11), 2671–2686.
   Web of Science ®Google Scholar
• Bersimis, S., Psarakis, S., & Panaretos, J. (2007). Multivariate statistical process control charts: An overview. Quality and Reliability Engineering International, 23(5), 517–543.
   Web of Science ®Google Scholar
• Cadwell, J. (1953). Approximating to the distribution of measures of dispersion by a power of chi-squares. Biometrika, 40, 336–346.
   Web of Science ®Google Scholar
• Celano, G., Castagliola, P., Faraz, A., & Fichera, S. (2014). Statistical performance of a control chart for individual observations monitoring the ratio of two normal variables. Quality and Reliability Engineering International, 30(8), 1361–1377.
   Web of Science ®Google Scholar
• Chen, G., Cheng, S. W., & Xie, H. (2005). A new multivariate control chart for monitoring both location and dispersion. Communications in Statistics: Simulation and Computation, 34(1), 203–217.
   Web of Science ®Google Scholar
• Chen, Y.-K. (2007a). Economic design of variable sampling interval T2 control charts - A hybrid Markov chain approach with genetic algorithms. Expert Systems with Applications, 33(3), 683–689.
   Web of Science ®Google Scholar
• Chen, Y.-K. (2007b). Economic design of an adaptive T2 control chart. Journal of the Operational Research Society, 58(3), 337–345.
   Web of Science ®Google Scholar
• Chen, Y.-K. (2009). Economic design of T2 control charts with the VSSI sampling scheme. Quality and Quantity, 43(1), 109–122.
   Web of Science ®Google Scholar
• Chen, Y.-K., & Hsieh, K.-L. (2007). Hotelling’s T2 charts with variable sample size and control limit. European Journal of Operational Research, 182(3), 1251–1262.
   Web of Science ®Google Scholar
• Chen, Y.-K., Hsieh, K.-L., & Chang, -C.-C. (2002). Economic design of the VSSI over(X,-) control charts for correlated data. International Journal of Production Economics, 107(2), 528–539.
   Google Scholar
• Chou, C.-Y., Chen, C.-H., & Chen, C.-H. (2006). Economic design of variable sampling intervals T2 control charts using genetic algorithms. Expert Systems Applications, 30(2), 233–242.
   Web of Science ®Google Scholar
• Chou, C.-Y., Chen, C.-H., Liu, H.-R., & Huang, X.-R. (2003). Economic-statistical design of multivariate control charts for monitoring the mean vector and covariance matrix. Journal of Loss Prevention in the Process Industries, 16(1), 9–18.
   Web of Science ®Google Scholar
• Costa, A. F. B., & Machado, M. A. G. (2008). A new chart for monitoring the covariance matrix of bivariate processes. Communications in Statistics - Simulation and Computation, 37(7), 1453–1465.
   Web of Science ®Google Scholar
• Costa, A. F. B., & Machado, M. A. G. (2009). A new chart based on sample variances for monitoring the covariance matrix of multivariate processes. International Journal of Advanced Manufacturing Technology, 41(7–8), 770–779.
   Web of Science ®Google Scholar
• Costa, A. F. B., & Machado, M. A. G. (2011a). A control chart based on sample ranges for monitoring the covariance matrix of multivariate processes. Journal of Applied Statistics, 38(2), 233–245.
   Web of Science ®Google Scholar
• Costa, A. F. B., & Machado, M. A. G. (2011b). Monitoring the mean vector and the covariance matrix of multivariate processes with sample means and sample ranges. Producao, 21(2), 197–208.
   Google Scholar
• Costa, A. F. B., & Machado, M. A. G. (2013). A single chart with supplementary runs rules for monitoring the mean vector and the covariance matrix of multivariate processes. Computers and Industrial Engineering, 66(2), 431–437.
   Web of Science ®Google Scholar
• Faraz, A., Chalaki, K., & Moghadam, M. B. (2011). On the properties of the Hotelling’s T2 control chart with variable sampling intervals. Quality and Quantity, 45(3), 579–586.
   Google Scholar
• Faraz, A., Heuchenne, C., Saniga, E., & Costa, A. F. B. (2014). Double-objective economic statistical design of the VP T2 control chart: Wald’s identity approach. Journal of Statistical Computation and Simulation, 84(10), 2123–2137.
   Web of Science ®Google Scholar
• Faraz, A., & Moghadam, M. B. (2009). Hotelling’s T2 control chart with two adaptive sample sizes. Quality and Quantity, 43(6), 903–912.
   Web of Science ®Google Scholar
• Guerrero-Cusumano, J.-L. (1995). Testing variability in multivariate quality control: A conditional entropy measure approach. Information Sciences, 86(1–3), 179–202.
   Web of Science ®Google Scholar
• Healy, J. D. (1987). A note on multivariate CUSUM procedures. Technometrics, 29(4), 409–412.
   Web of Science ®Google Scholar
• Hotelling, H. (1947). Multivariate quality control—Illustrated by the air testing of sample bombsights. In C. Eisenhart, M. W. Hastay, & W. A. Wallis (Eds.), Techniques of statistical analysis (pp. 111–184). New York, NY: MacGraw Hill.
   Google Scholar
• Jackson, J. E. (1985). Multivariate quality control. Communications in Statistics-Theory and Methods, 14(11), 2657–2688.
   Web of Science ®Google Scholar
• Khoo, M. B. C. (2005). A new bivariate control chart to monitor the multivariate process mean and variance simultaneously. Quality Engineering, 17(1), 109–118.
   Google Scholar
• Lee, M. H., & Khoo, M. B. C. (2015). Multivariate synthetic |S| Control chart with variable sampling interval. Communications in Statistics: Simulation and Computation, 44(4), 924–942.
   Web of Science ®Google Scholar
• Li, B., Wang, K., & Yeh, A. B. (2013). Monitoring the covariance matrix via penalized likelihood estimation. IIE Transactions, 45(2), 132–146.
   Web of Science ®Google Scholar
• Liu, L.-P., Zhong, J.-L., & Ma, Y.-Z. (2013). A multivariate synthetic control chart for monitoring covariance matrix based on conditional entropy. In E. Qi., J. Shen., & R. Dou, (Eds.), 19th International Conference on Industrial Engineering and Engineering Management (pp. 99–107). Berlin/Heidelberg: Springer.
   Google Scholar
• Machado, M. A. G., Costa, A. F. B., & Marins, F. A. S. (2009b). Control charts for monitoring the mean vector and the covariance matrix of bivariate processes. International Journal of Advanced Manufacturing Technology, 45(7–8), 772–785.
   Web of Science ®Google Scholar
• Machado, M. A. G., Costa, A. F. B., & Rahim, M. A. (2009a). The synthetic control chart based on two sample variances for monitoring the covariance matrix. Quality and Reliability Engineering International, 25(5), 595–606.
   Web of Science ®Google Scholar
• Memar, A. O., & Niaki, S. T. A. (2009). New control charts for monitoring covariance matrix with individual observations. Quality and Reliability Engineering International, 25(7), 821–838.
   Web of Science ®Google Scholar
• Mitra, A., & Clark, M. (2014). Monitoring variability of multivariate processes. International Journal of Quality Engineering and Technology, 4(2), 112–132.
   Google Scholar
• Montgomery, D. C. (2009). Introduction to statistical quality control (6th ed.). USA: Wiley.
   Google Scholar
• Nenes, G., & Tagaras, G. (2006). The economically designed CUSUM chart for monitoring short production runs. International Journal of Production Research, 44(8), 1569–1587.
   Web of Science ®Google Scholar
• Park, C., & Reynolds, M. R. (1999). Economic design of a variable sampling rate Xbar chart. Journal of Quality Technology, 31, 427–443.
   Web of Science ®Google Scholar
• Reynolds, M. R., & Cho, G.-Y. (2006). Multivariate control charts for monitoring the mean vector and covariance matrix. Journal of Quality Technology, 38(3), 230–253.
   Web of Science ®Google Scholar
• Reynolds, M. R., & Cho, G.-Y. (2011). Multivariate control charts for monitoring the mean vector and covariance matrix with variable sampling intervals. Sequential Analysis, 30(1), 1–40.
   Web of Science ®Google Scholar
• Reynolds, M. R., & Stoumbos, Z. G. (2008). Combinations of multivariate Shewhart and MEWMA control charts for monitoring the mean vector and covariance matrix. Journal of Quality Technology, 40(4), 381–393.
   Web of Science ®Google Scholar
• Spiring, F. A., & Cheng, S. W. (1998). An alternate variables control chart: The univariate and multivariate case. Statistica Sinica, 8(1), 273–287.
   Web of Science ®Google Scholar
• Sullivan, J. H., & Woodall, W. H. (2000). Change-point detection of mean vector or covariance matrix shifts using multivariate individual observations. IIE Transactions, 32(6), 537–549.
   Web of Science ®Google Scholar
• Tagaras, G., & Lee, H. L. (1988). Economic design of control charts with different control limits for different assignable causes. Management Science, 34(11), 1347–1366.
   Web of Science ®Google Scholar
• Tasias, A. K., & Nenes, G. (2018a). Economic-statistical design of a variable parameter control scheme for joint monitoring of mean and variance of processes subject to multiple assignable causes. Quality Technology and Quantitative Management, 15(4), 484–506.
   Web of Science ®Google Scholar
• Tasias, A. K., & Nenes, G. (2018b). Optimization of a fully adaptive quality & maintenance model in the presence of multiple location and scale quality shifts. Applied Mathematical Modelling, 54, 64–81.
   Web of Science ®Google Scholar
• Thaga, K. (2004). Contributions to statistical process control tools (Doctoral dissertation). University of Manitoba, Canada.
   Google Scholar
• Wang, K., Yeh, A. B., & Li, B. (2014). Simultaneous monitoring of process mean vector and covariance matrix via penalized likelihood estimation. Computational Statistics and Data Analysis, 78, 206–217.
   Web of Science ®Google Scholar
• Yang, W.-A. (2016). Simultaneous monitoring of mean vector and covariance matrix shifts in bivariate manufacturing processes using hybrid ensemble learning-based model. Journal of Intelligent Manufacturing, 27(4), 845–874.
   Web of Science ®Google Scholar
• Yeh, A. B., & Lin, D. K.-J. (2002). A new variables control chart for simultaneously monitoring multivariate process mean and variability. International Journal of Reliability, Quality and Safety Engineering, 9(1), 41–59.
   Google Scholar
• Yeh, A. B., Lin, D. K.-J., & McGrath, R. N. (2006). Multivariate control charts for monitoring covariance matrix: A review. Quality Technology and Quantitative Management, 3(4), 415–436.
   Google Scholar
• Yeh, A. B., & Wang, K. (2012). Monitoring multivariate process variability with individual observations via penalised likelihood estimation. International Journal of Production Research, 50(22), 6624–6638.
   Web of Science ®Google Scholar