Robustness Comparison of Exponentially Weighted Moving-Average Charts on Autocorrelated Data and on Residuals

References

• Adams, B. M. and Tseng, I. T. (1998). “Robustness of Forecast-Based Monitoring Schemes”. Journal of Quality Technology 30 (4), pp. 328–339.
   Web of Science ®Google Scholar
• Alwan, L. C. (1992). “Effects of Autocorrelation on Control Chart Performance”. Communications in Statistics: Theory and Methods 21, pp. 1025–1049.
   Web of Science ®Google Scholar
• Alwan, L. C. and Roberts, H. V. (1988). “Time-Series Modeling for Statistical Process Control”. Journal of Business and Economic Statistics 6 (1), pp. 87–95.
   Web of Science ®Google Scholar
• Apley, D. W. (2002). “Time Series Control Charts in the Presence of Model Uncertainty”. ASME Journal of Manufacturing Science and Engineering 124 (4), pp. 891–898.
   Web of Science ®Google Scholar
• Apley, D. W. and Chin, C. H. (2007). “An Optimal Filter Design Approach to SPC for Autocorrelated Processes”. Journal of Quality Technology 39 (2), pp. 93–117.
   Web of Science ®Google Scholar
• Apley, D. W. and Lee, H. C. (2003). “Design of Exponentially Weighted Moving Average Control Charts for Autocorrelated Processes With Model Uncertainty”. Technometrics 45 (3), pp. 187–198.
   Web of Science ®Google Scholar
• Apley, D. W. and Lee, H. C. (2008). “The Effects of Model Parameter Deviations on the Variance of a Linearly Filtered Time Series”, submitted for publication.
   Google Scholar
• Apley, D. W. and Shi, J. (1999). “The GLRT for Statistical Process Control of Autocorrelated Processes”. IIE Transactions 31 (12), pp. 1123–1134.
   Web of Science ®Google Scholar
• Åström, K. J. and Wittenmark, B. (1990). Computer Controlled Systems: Theory and Design, 2nd edition. Prentice Hall, Englewood Cliffs, NJ.
   Google Scholar
• Berthouex, P. M.; Hunter, W. G.; and Pallesen, L. (1978). “Monitoring Sewage Treatment Plants: Some Quality Control Aspects”. Journal of Quality Technology 10, pp. 139–149.
   Web of Science ®Google Scholar
• Box, G.; Jenkins, G.; and Reinsel, G. (1994). Time Series Analysis, Forecasting, and Control, 3rd edition. Prentice-Hall, Englewood Cliffs, NJ.
   Google Scholar
• Chin, C. H. and Apley, D. W. (2006). “Optimal Design of Second-Order Linear Filters for Control Charting”. Technometrics 48 (3), pp. 337–348.
   Web of Science ®Google Scholar
• Del Castillo, E. (2002). Statistical Process Adjustment for Quality Control. Wiley, New York, NY.
   Google Scholar
• English, J. R.; Lee, S.; Martin, T. W.; and Tilmon, C. (2000). “Detecting Changes in Autoregressive Processes with Xˉ and EWMA charts”. IIE Transactions 32 (12), pp. 1103–1113.
   Web of Science ®Google Scholar
• Harris, T. J. and Ross, W. H. (1991). “Statistical Process Control Procedures for Autocorrelated Observations”. Canandian Journal of Chemical Engineering 69, pp. 48–57.
   Web of Science ®Google Scholar
• Johnson, R. A. and Bagshaw, M. (1974). “The Effect of Serial Correlation on the Performance of CUSUM tests”. Technometrics 16 (1), 103–112.
   Web of Science ®Google Scholar
• Kramer, H. and Schmid, W. (1997). “Control Charts for Time Series”. Nonlinear Analysis, Theory, Methods & Applications 30 (7), pp. 4007–4016.
   Web of Science ®Google Scholar
• Kramer, H. and Schmid, W. (2000). “The Influence of Parameter Estimation on the ARL of Shewhart Type Charts for Time Series”. Statistical Papers 41, pp. 173–196.
   Web of Science ®Google Scholar
• Lee, H. C. (2004). “Robust Design of Control Charts for Autocorrelated Processes with Model Uncertainty”. Unpublished Ph.D. Dissertation, Texas A&M University.
   Google Scholar
• Lin, W. S. and Adams, B. M. (1996). “Combined Control Charts for Forecast-Based Monitoring Schemes”. Journal of Quality Technology 28 (3), pp. 289–301.
   Web of Science ®Google Scholar
• Lu, C. W. and Reynolds, M. R., Jr. (1999). “EWMA Control Charts for Monitoring the Mean of Autocorrelated Processes”. Journal of Quality Technology 31 (2), pp. 166–188.
   Web of Science ®Google Scholar
• Lu, C. W. and Reynolds, M. R., Jr. (2001). “CUSUM Charts for Monitoring An Autocorrelated Process”. Journal of Quality Technology 33, pp. 316–334.
   Web of Science ®Google Scholar
• Lucas, J. M. and Saccucci, M. S. (1990). “Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements”. Technometrics 32 (1), pp. 1–12.
   Web of Science ®Google Scholar
• Montgomery, D. C. and Mastrangelo, C. M. (1991). “Some Statistical Process Control Methods for Autocorrelated Data”. Journal of Quality Technology 23 (3), pp. 179–193.
   Web of Science ®Google Scholar
• Montgomery, D. C. and Woodall, W. H. (1997). “A Discussion on Statistically-Based Process Monitoring and Control”. Journal of Quality Technology 29 (2), pp. 121–162.
   Web of Science ®Google Scholar
• Pandit, S. M. and Wu, S. M. (1990). Time Series and System Analysis with Applications, 2nd edition. Krieger, Malabar, FL.
   Google Scholar
• Roberts, S. W. (1959). “Control Chart Tests Based on Geometric Moving Averages”. Technometrics 1, pp. 239–250.
   Google Scholar
• Runger, G. C.; Willemain, T. R.; and Prabhu, S. (1995). “Average Run Lengths for Cusum Control Charts Applied to Residuals”. Communications in Statistics: Theory and Methods 24 (1), pp. 273–282.
   Web of Science ®Google Scholar
• Schmid, W. (1997). “On EWMA Charts for time Series”. In: Lenz, H. J. and Wilrich, P.-Th. (eds.), Frontiers of Statistical Quality Control, vol. 5, pp. 115–137. Physica-Verlag, Heidelberg.
   Google Scholar
• Schmid, W. and Schöne, A. (1997). “Some Properties of the EWMA Control Chart in the Presence of Autocorrelation”. Annals of Statistics 25 (3), pp. 1277–1283.
   Web of Science ®Google Scholar
• Schöne, A.; Schmid, W.; and Knoth, S. (1999). “On the Run Length of the EWMA Scheme: A Monotonicity Result for Normal Variables”. Journal of Statistical Planning and Inference 79, pp. 289–297.
   Web of Science ®Google Scholar
• Stoumbos, Z. G.; Reynolds, M. R., Jr.; Ryan, T. P.; and Woodall, W. H. (2000). “The State of Statistical Process Control as We Proceed into the 21st Century”. Journal of the American Statistical Association 95, pp. 992–998.
   Web of Science ®Google Scholar
• Superville, C. R. and Adams, B. M. (1994). “An Evaluation of Forecast-Based Quality Control Schemes”. Communications in Statistics: Simulation and Computation 23 (3), pp. 645–661.
   Web of Science ®Google Scholar
• Vander Weil, S. A. (1996). “Monitoring Processes That Wander Using Integrated Moving Average Models”. Technometrics 38 (2), pp. 139–151.
   Web of Science ®Google Scholar
• Vasilopoulos, A. V. and Stamboulis, A. P. (1978). “Modification of Control Chart Limits in the Presence of Data Correlation”. Journal of Quality Technology 10 (1), pp. 20–30.
   Web of Science ®Google Scholar
• Wardell, D. G.; Moskowitz, H.; and Plante, R. D. (1994). “Run-Length Distributions of Special-Cause Control Charts for Correlated Processes”. Technometrics 36 (1), pp. 3–17.
   Web of Science ®Google Scholar
• Woodall, W. H. and Montgomery, D. C. (1999). “Research Issues and Ideas in Statistical Process Control”. Journal of Quality Technology 31 (4), pp. 376–386.
   Web of Science ®Google Scholar
• Zhang, N. F. (1998). “A Statistical Control Chart for Stationary Process Data”. Technometrics 40 (1), pp. 24–38.
   Web of Science ®Google Scholar