References
- Alwan, L. C. and Roberts, H. V. (1988). Time-series modeling for statistical process control. Journal of Business & Economics Statistics, 6(1), 87–95.
- Alwan, L. C. (1991). Autocorrelation: fixed versus variable control limits. Quality Engineering, 4(2), 167–188.
- Alwan, L. C. (1992). Effects of autocorrelation on control chart performance. Communications in Statistics — Theory and Methods, 21(4), 1025–1049.
- Alwan, A. J. and Alwan, L. C. (1994). Monitoring autocorrelated processes using multivariate quality control charts. Proceedings of the Decision Sciences Institute Annual Meeting, 3, 2106–2108.
- Alwan, L. C., Champ, C. W. and Maragah, H. D. (1994). Study of average run lengths for supplementary runs rules in the presence of autocorrelation. Communications in Statistics — Simulation and Computation, 23(2), 373–391.
- Amin, R. W., Schmid, W. and Frank, O. (1997). The effects of autocorrelation on the R-chart and the S2-chart. Sankhya ser. B, 59(3), 229–255.
- Amin, R. W. and Lee, S. J. (1999). The effects of autocorrelation and outliers on two-sided tolerance limits. Journal of Quality Technology, 31(3), 286–300.
- Amin, R. W. Wolff H., Besenfelder, W. and Baxley, Jr. R. (1999). An EWMA control chart for the smallest and largest observations. Journal of Quality Technology, 31(2), 189–206.
- Amin, R. W. and Li, K. (2002). The effect of autocorrelation on the EWMA maxmin tolerance limits. Journal of Statistical Computation and Simulation, 72(9), 719–735.
- Aoki, M. (1983). Notes on economic time series analysis: system theoretic approach. Heidelberg Germany Springer-Verlag
- Apley, D. W. and Lee, H. C. (2003). Design of exponentially weighted moving average control charts for auocorrelated processes with model uncertainty. Technometrics, 45(3), 187–198.
- Apley, D. W. and Shi, J. (1999). The GLRT for statistical process control of autocorrelated processes. IIE Transactions, 31(12), 1123–1134.
- Apley, D. W. and Tsung, F. (2002). The autoregressive T2 chart for monitoring univariate autocorrelated processes. Journal of Quality Technology, 34(1), 80–96.
- Aradhye, H. B., Bakshi, B. R., Strauss, R. A. and Davis, J. F. (2003). Multiscale SPC using wavelets: theoretical analysis and properties. AICHE Journal, 49(4), 939–958.
- Atienza, O. O., Tang, L. C. and Ang, B. W. (1997). ARL properties of sample autocorrelation chart. Computers Ind. Eng., 33(3–4), 733–736.
- Atienza, O. O., Tang, L. C. and Ang, B. W. (2002). A CUSUM scheme for autocorrelated observations. Journal of Quality Technology, 34(2), 187–199.
- Bagshaw, M. and Johnson, R. A. (1975). The effect of serial correlation on the performance of CUSUM tests II. Technometrics, 17(1), 73–80.
- Ben-Gal, I., Morag, G. and Shmilovici, A. (2003). Context-based statistical process control: a monitoring procedure for state-dependent processes. Technometrics, 45(4), 293–311.
- Ben-Gal, I. and Singer, G. (2004). Statistical process control via context modeling of finite-state processes: an application to production monitoring. IIE Transactions, 36(5), 401–415.
- Bhat, U. N. and Lal, R. (1990). Attribute control charts for Markov dependent production processes. IIE Trancactions, 22(2), 181–188.
- Bishop, C. M. (1995). Neural networks for pattern recognition, Oxford. Oxford University Press.
- Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1994). Time Series Analysis, Forecasting and Control, 3rd edition. Prentice Hall, Englewood Cliffs, N. J.
- Box, G. E. P. and Luceno, A. (1997). Statistical Control by Monitoring and Feedback Adjustment. John Wiley & Sons, New York.
- Boyles, R. A. (2000). Phase I analysis for autocorrelated processes. Journal of Quality Technology, 32(4), 395–409.
- Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. Springer-Verlag, New York.
- Buhmann, M. D. (2003). Radial Basis Functions: Theory and Implementations. Cambridge University Press.
- Castagliola, P. and Tsung, F. (2005). Autocorrelated SPC for non-normal situations. Quality and Reliability Engineering International, 21(2), 131–161.
- Chan, L. K. and Li, G. Y. (1994). A multivariate control chart for detecting linear trends. Communications in Statistics — Simulation and Computation, 23(4), 997–1012.
- Charnes, J. M. (1995). Tests for special causes with multivariate autocorrelated data. Computers and Operational Research, 22(4), 443–453.
- Chen, Y. K. and Chiu, K. C. (2005). Optimal design of VSI X control charts for monitoring correlated samples. Quality and Reliability Engineering International, 21(8), 757–768.
- Cheng, S. W. and Thaga, K. (2005). Max-CUSUM chart for autocorrelated processes. Statistica Sinica, 15(2), 527–546.
- Chinnam, R. B. (2002). Support vector machines for recognizing shifts in correlated and other manufacturing processes. International Journal of Production Research, 40(17), 4449–4466.
- Chiu, C. C., Chen, M. K. and Lee, K. R. (2001). Shift recognition in correlated process data using a neural network. International Journal of System Science, 32(2), 137–143.
- Clayton, H. R., Harvey, M. M. and Prybutok, V. R. (1997). Simulation study for comparing fixed with variable sampling interval Shewhart X-bar control charts in the presence of undetected autocorrelated data. Simulation, 68(3), 164–174.
- Cook, D. F. and Chiu, C. (1998). Using radial basis function neural networks to recognize shifts in correlated manufacturing parameters. IIE Transactions, 30(3), 227–234.
- Cook, D. F. Zobel, C. W. and Nottingham, Q. J. (2001). Utilization of neural networks for the recognition of variance shifts in correlated manufacturing process parameters. International Journal of Production Research, 39(17), 3881–3887.
- Crowder, S.V., Eshleman, L. (2001). Small sample properties of an adaptive filter applied to low volume SPC. Journal of Quality Technology, 33(1), 29–46.
- Deligonul and Mergen (1987). Dependence bias in conventional p-charts and its correction with an appropriate lot quality distribution. Journal of Applied Statistics, 14(1), 75–81.
- Desieno, D. (1988). Adding a conscience to competitive learning. Proceedings, International Joint Conference on Neural Networks, 1, 117–124.
- Dooley, K. J. and Kapoor, S. G. (1990). An enhanced quality evaluation system for continuous manufacturing processes. Journal of Engineering for Industry_Transactions of the ASME, 112(1), 57–62.
- Dyer, J. N., Adams, B. M. and Conerly, M. D. (2003). The reverse moving average control chart for monitoring autocorrelated processes. Journal of Quality Technology, 35 (2), 139–152.
- Dyer, J. N., Conerly, M. D. and Adams, B. M. (2003). A simulation study and evaluation of multivariate forecast based control charts applied to ARMA processes. Journal of Statistical Computations and Simulation, 73(10), 709–724.
- English, J. R. Lee, S. C. Martin,_T. W. and Tilmon, C. (2000). Detecting changes in autoregressive processes with X and EWMA charts. IIE Transactions, 32(12), 1103–1113.
- English, J. R. and Alam, J. (2001). Modelling and process disturbance detection of autocorrelated data. Nonlinear Analysis, 47(3), 2103–2111.
- Fong, D. Y. T. and Lawless, J. F. (1998). The analysis of process variation transmission with multivariate measurements. Statistica Sinica, 8(1), 151–164.
- Hambourg, J. H., Booth, D. E. and Weinroth, G. J. (1996). A neural network approach to the detection of nuclear material losses. Journal of Chemical Information and Computer Sciences, 36(3), 544–553.
- Han, D. and Tsung, F. (2005). Comparison of the cuscore, GLRT and CUSUM control charts for detecting dynamic mean change. Annals of the Institute of Statistical Mathematics, 57(3), 531–522.
- Harris, T. J. and Ross, W. H. (1991). Statistical process control procedures for correlated observations. Canadian Journal of Chemical Engineering, 69(1), 48–57.
- Harvey, A. C. and Fernandes, C. (1989). Time series modeling for count or correlated observations. Journal of Business and Economic Statistics, 7(4), 407–422.
- Haykin, S. (1994). Neural Networks, A Comprehensive Foundation. Macmillan, New York, NY.
- Henderson, G. R. (2001). EWMA and industrial applications to feedback adjustment and control. Journal of Applied Statistics, 28 (3–4), 399–407.
- Hotelling, H. (1947). Multivariate quality control — illustrated by the air testing of sample bombsights. Techniques of Statistical Analysis, Eisenhart, C., Hastay, M. W., Wallis, W. A. (eds), New York: MacGraw-Hill, 111–184.
- Hwarng, H. B. (2004). Detecting process mean shift in the presence of autocorrelation: a neural network based monitoring scheme. International Journal of Production Research, 42(3), 573–595.
- Hwarng, H. B. (2005). Simultaneous detection of mean shift and correlation change in AR(1) processes. International Journal of Production Research, 43 (9), 1761–1783.
- Jackson, J. E. (1991). A User Guide to Principal Components. John Wiley: New York 1991.
- Jiang, W., Tsui, K. L. and Woodall, W. H. (2000). A new SPC monitoring method: The ARMA chart. Technometrics, 42(4), 399–410.
- Jiang, W. (2001). Average run length computation of ARMA charts for stationary processes. Communications in Statistics — Simulation and Computation, 30(3), 699–716.
- Jiang, W. and Tsui, K. L. (2001). Some properties of the ARMA control chart. Nonlinear Analysis, 47(3), 2073–2088.
- Jiang, W. Wu, H., Tsung, F., Nair, V. N. and Tsui, K.-L. (2002). Proportional integraql derivative charts for process monitoring. Technometrics, 44 (3), 205–214.
- Jiang, W. (2004). Multivariate control charts for monitoring autocorrelated processes. Journal of Quality Technology, 36(4), 367–379.
- Johnson, N. L. (1949). Systems of frequency curves generated by methods of translation. Biometrika, 36 (1–2), 149–176.
- Johnson, R. A. and Bagshaw, M. (1974). The effect of serial correlation on the performance of CUSUM tests. Technometrics,16 (1), 103–112.
- Kalgonda, A. A. and Kulkarni, S. R. (2004). Multivariate quality control chart for autocorrelated processes. Journal of Applied Statistics, 31 (3), 317–327.
- Knoth, S. and Amin, R. W. (2003). Autocorrelation and tolerance limits. Journal of Statistical Computation and Simulation, 73(7), 719–735.
- Knoth, S. and Schmid, W. (2004). Control charts for time series: a review. Frontiers in Statistical Quality Control V.6. Lenz, H.-J. and Wilrich, P.-Th. (Eds.), 7. Physica-Verlag, Heidelburg.
- Kourti, T. (2003). Multivariate dynamic modelling for analysis and statistical process control of batch processes, start-ups and grade transitions. Journal of Chemometrics, 17(1), 93–109.
- Kramer, H. and Schmid, W. (1997). Control charts for time series. Nonlinear Analysis, 30(7), 4007–4016.
- Ku, W., Storer, R. H. and Georgakis, C. (1995). Disturbance detection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems, 30(1), 179–196.
- Lai, C. D., Govindaraju, K. and Xie, M. (1998). Effects of correlation on fraction non-conforming statistical process control procedures. Journal of Applied Statistics, 25 (4), 535–543.
- Lai, C. D., Govindaraju, K. and Xie, M. (2000). Study of a Markov model for a high quality dependent process. Journal of Applied Statistics, 27(4), 461–473.
- Lieftucht, D., Kruger, U., Xie, L., Littler, T., Chen, Q. and Wang, S. (2006). Statistical monitoring of dynamic multivariate processes-part 2. Identifying fault magnitude and signature. International Engineering Chemistry Research, 45(5), 1677–1688.
- Lina, W. K., Woodall, H. W. and Busby, L. K. (2001). The performance of multivariate control charts in the presence of measurement error. Journal of Quality Technology, 33 (3), 349–355.
- Liu, H. R. Chou, C. Y. and Chen, C. H. (2003). The effect of correlation on the economic design of warning limit X-bar charts. International Journal of Advanced Manufacturing Technology, 22(3–4), 306–312.
- Longnecker, M. T. and Ryan, T. P. (1991). A Deficiency for Residuals Charts for Correlated Data. Technical Report, 166. Texas A&M University, Department of Statistics.
- Longnecker, M. T. and Ryan, T. P. (1992). Charting Correlated Process Data. Technical Report, No.166. Texas A&M University, Department of Statistics.
- Loredo, E. N., Jearkpaporn, D. and Borror, C. M. (2002). Model based control chart for autoregressive and correlated data. Quality and Reliability Engineering International, 18(6), 489–496.
- Low, C., Hsu, C. M. and Yu, F. J. (2003). Analysis of variations in a multi-variate process using neural networks. International Journal of Advanced Manufacturing Technology, 22(11–12), 911–921.
- Lu, C. W. and Reynolds, M. R. Jr. (1999a). EWMA control charts for monitoring the mean of autocorrelated processes. Journal of Quality Technology, 31(2), 166–188.
- Lu, C. W. and Reynolds, M. R. Jr. (1999b). Control charts for monitoring the mean and the variance of autocorrelated processes. Journal of Quality Technology, 31(3), 259–274.
- Lu, C. W. and Reynolds, M. R. Jr. (2001). CUSUM Charts for Monitoring an Autocorrelated Process. Journal of Quality Technology, 33(3), 316–334.
- Luceno, A. and Box, G. P. (2000). Influence of the sampling interval, decision limit and autocorrelation on the average run length in CUSUM charts. Journal of Applied Statistics, 27(2), 177–183.
- MacGregor, J. F. and Harris, T. J. (1993). The exponentially weighted moving variance. Journal of Quality Technology, 25(2), 106–118.
- Mahmoud, M. A. and Woodall, W. H. (2004). Phase I analysis of linear profiles with calibration applications. Technometrics, 46(4), 380–391.
- Mason, R. L., Tracy, N. D. and Young, J. C. (1996). Monitoring a multivariate step process. Journal of Quality Technology, 28(1), 39–50.
- Mason, R. L., Chou, Y. M., Sullivan, J. H. Stoumbos, Z. G. and Young, J. C. (2003). Systematic Patterns in T2 charts. Journal of Quality Technology, 35(1), 47–58.
- Mastrangelo, C. M. and Forrest, D. R. (2002). Multivariate autocorrelated processes: Data and shift generation. Journal of Quality Technology, 34(2), 216–220.
- Mastrangelo, C. M. and Montgomery, D. C. (1995). SPC with correlated observations for the chemical and process industries. International Journal of Reliability, Quality and Safety Engineering, 11 (2), 79–89.
- Mastrangelo, M. C., Runger, G. C. and Montgomery, D. C. (1996). Statistical process monitoring with principal components. Quality and Reliability Engineering International, 12(3), 203–210.
- Moguerza, J. M. and Munoz, A. (2006). Support vector machines with applications. Statistical Science, 21(3), 322–336.
- Montgomery, D. C. and Mastrangelo, C. M. (1991). Some statistical process control methods for autocorrelated data. Journal of Quality Technology, 23(3), 179–204.
- Montgomery, D. C. (2001). Introduction to Statistical Quality Control. John Wiley & Sons, New York.
- Moody, J. and Darken, C. J. (1989). Fast learning in networks of locally-tuned processing units. Neural Computation,1, 281–293.
- Noorossana, R., Farrokhi, M. and Saghaei, A. (2003). Using neural networks to detect and classify out-of control signals in autocorrelated processes. Quality and Reliability Engineering International, 19(6), 493–504.
- Nembhard, H. B. (1998). Simulation using state-space representation of noisy dynamic systems to determine effective integrated process control designs. IIE Transactions, 30(3), 247–256.
- Nembhard, H. B. and Mastrangelo, C. M. (1988). Integrated process control for startup operations. Journal of Quality Technology, 30(3), 201–211.
- Nembhard, H. B. and Kao, M. S. (2003). Adaptive forecast-based monitoring for dynamic systems. Technometrics, 45(3), 208–219.
- Nieckula, J. and Hryniewicz, O. (1997). Neural network support for Shewhart X-bar control chart. Systems Science, 23(1), 154–157.
- Ogata, K. (1990). Modern Control Engineering, 2nd edition. Prentice-Hall, Englewood Cliffs, NJ.
- Padgett, C. S. Thombs, L. A. and Padgett, W. J. (1992). On the a-risks for Shewhart control charts. Communications in Statistics — Simulation and Computation, 21(4), 1125–1147.
- Pan, X. and Jarrett, J. (2004). Applying state space to SPC: monitoring multivariate time series. Journal of Applied Statistics, 31 (4): 397–418.
- Pandit, S. M. and Wu, S. (1983). Time Series and System Analysis with Applications. John Wiley & Sons, New York.
- Papaleonida, G. (2002). Control Charts for Autocorrelated Processes. MSc Thesis Athens University of Economics and Business ISBN: 960-8287-03-0.
- Papaleonida, G. and Psarakis, S. (2002). Control charts for Monitoring Autocorrelated Processes. Athens University of Economics and Business Technical Report 175 June 2002.
- Park, C. (2001). A Statistical process control procedure with adjustments and monitoring. Nonlinear Analysis, 47(3), 2061–2072.
- Pawlak, M., Rafajlowicz, E. and Steland, A. (2004). On detecting jumps in time series: Nonparametric settings. Robustness of Measures of Common Cause Sigma in Presence of Data Correlation Nonparametric Statistics, 16(3–4) 329–347.
- Prasad, S. S. R. and Bhadury, B. (2004). Robustness of measures of common cause sigma in presence of data correlation. Journal of Statistical Computation and Simulation, 74(5), 313–338.
- Prybutok, V. R., Clayton, H. R. and Harvey, M. M. (1997). Comparison of fixed versus variable sampling interval Shewhart control charts in the presence of positive autocorrelated data. Communications in Statistics-Simulation and Computation, 26(1), 83–106.
- Quesenberry, C. P. (1991) SPC-Q-charts for start-up processes and short or long runs. Journal of Quality Technology, 23(3), 47–58.
- Ramjee, R., Crato, N. and Ray, B. K. (2002). A note on moving average forecasts of long memory processes with an application to quality control. International Journal of Forecasting, 18(2), 291–297.
- Reynolds, M. R. Jr., Amin, R.W., Arnold, J. C. and Nachlas, J. A. (1988). X charts with variable sampling intervals. Technometrics, 30 (2), 181–192.
- Reynolds, M. R. Jr. (1995). Evaluating properties of variable sampling interval control charts. Sequential Analysis, 14 (1), 59–97.
- Reynolds, M. R. Jr., Arnold, J. C. and Baik, J. W. (1996). Variable sampling interval charts in the presence of correlation. Journal of Quality Technology, 28(1), 12–30.
- Roberts, S. N. (1959). Control chart tests based on geometric moving averages, Technometrics, 1(3), 239–250.
- Rosolowski, M. and Schmid, W. (2006). EWMA charts for monitoring the mean and autocovariances of stationary processes. Statistical Papers, 47(4), 595–630.
- Runger, G. C. (1996). Multivariate statistical process control for autocorrelated processes. International Journal of Production Research, 34(6), 1715–1724.
- Runger, G. C. (2002). Assignable causes and autocorrelation: control charts for observations or residuals? Journal of Quality Technology, 34(2), 165–170.
- Runger, G. C. and Testik, M. C. (2003). Control charts for monitoring fault signatures: Cuscore versus GLR. Quality and Reliability Engineering International, 19(4), 387–396.
- 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), 273–282.
- Runger, G. C. and Willemain, T. R. (1996). Batch means control charts for autocorrelated data. IIE Transactions, 28(6), 483–487.
- Runger, G. C. and Willemain, T. R. (1995). Model-based and model-free control of autocorrelated processes. Journal of Quality Technology, 27(4), 283–292.
- Ryan, TP. (1991). Discussion of Some statistical process control methods for autocorrelated Data by D.C. Montgomery and C.M. Mastrangelo. Journal of Quality technology, 23 (3), 200–202.
- Ryan, TP. (2000). Statistical Methods for Quality Improvement, 2nd edition. John Wiley & Sons, New York.
- Schmid, W. (1995). On the run length of a Shewhart chart for correlated data. Statistical Papers, 36(2), 111–130.
- Schmid, W. (1997a). On EWMA charts for time series. Frontiers of Statistical Quality Control 5. Edited by H.J. Lenz and P.-Th. Wilrich. Physica-Verlag, Heidelburg.
- Schmid, W. (1997b). CUSUM control schemes for gaussian processes. Statistical Papers, 38(2), 191–217.
- Schmid, W. and Schone, A. (1997). Some properties of the EWMA control chart in the presence of autocorrelation. Annals of Statistics, 25(3), 1277–1283.
- Shepherd, D. K., Champ, C. W., Ridgon, S. E. and Fuller, H. T. (2006). Attribute charts for monitoring a dependent process. Quality and Reliability Engineering International, in press.
- Shu, L., Apley, D. W. and Tsung, F. (2002). Autocorrelated process monitoring using triggered cuscore charts. Quality and Reliability Engineering International,18(5), 411–421.
- Snoussi, A. El Ghourabi, M. and Limam, M. (2005). On SPC for short run autocorrelated data. Communications in Statistics — Simulation and Computation, 34(1), 219–234.
- Sparks, R. S. (2000). CUSUM charts for AR1 data: are they worth the effort? Australian & New Zealand Journal of Statistics, 42(1), 25–42.
- Stimson, W. A. and Mastrangelo, C. M. (1996). Monitoring serially-dependent processes with attribute data. Journal of Quality Technology, 28(3), 279–288.
- Stoubos, Z. G. and Reynolds, M. R. (2000). Robustness to non-normality and autocorrelation of individuals control charts. Journal of Statistical Computation and Simulation, 66(2), 145–187.
- Tang, L.-C. and Cheong, W.-T. (2006). A control scheme for high-yield correlated production under group inspection. Journal of Quality Technology, 38(1), 45–56.
- Terpstra, J. T., McKean, J. W. and Anderson, K. (2003). Studentized autoregressive time series residuals. Computational Statistics, 18(1), 123–141.
- Testik, M. C. (2005). Model inadequacy and residuals control charts for autocorrelated processes. Quality and Reliability Engineering International, 21(2), 115–130.
- Theodossiou, P. T. (1993). Predicting shifts in the mean of multivariate time series process: an application in predicting business failures. Journal of the American Statistical Association, 88(422), 441–447.
- Timmer, D. H., Pigniatiello, Jr. and Longnecker, M. (1998). The development and evaluation of CUSUM-based control charts for an AR(1) process. IIE Transactions, 30(6), 525–534.
- Timmer, D. H. and Pigniatiello, Jr. (2003). Change point estimates for the parameters of an AR(1) process. Quality and Reliability Engineering International, 19(4), 355–369.
- Triantafyllpoulos, K. (2006). Multivariate control charts based on Bayesian state space models. Quality and Reliability Engineering International, 22(6), 693–707.
- Tsung, F. and Tsui, K. L. (2003). A mean-shift pattern study on integration of SPC and APC for process monitoring. IIE Transactions, 35(3), 231–242.
- Tsung, F., Zhao, Y., Xiang, L. and Jiang, W. (2006). Improved design of proportional integral derivative charts. Journal of Quality Technology, 38(1), 31–43.
- Vander, W., S. A. (1996). Monitoring Processes that wander using integrated moving average models. Technometrics, 38(2), 139–151.
- VanBrackle, L. N. and Reynolds, M. R. Jr. (1997). E WMA and CUSUM Control Charts in the Presence of Correlation. Communications in Statistics-Simulation and Computation, 26(4), 979–1008.
- Vapnik, V. (1979). Estimation of Dependencies Based on Empirical Data (in Russian), Moscow: Nauka. (English translation: New York: Springer Verlag, 1982).
- Vapnik, V. (1995). The Nature of Statistical Learning Theory. New York: Springer Verlag.
- 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), 20–30.
- Wade, R. and Woodall, W. (1993). A review and analysis of cause-selecting control charts. Journal of Quality Technology, 25 (3), 161–188.
- Wang, F. K. (2005). A simple data transformation for auto-correlated data for SPC. International Journal of Production Research, 43(5), 981–989.
- Wardell, D. G., Moskowitz, H. and Plante, R. D. (1992). Control charts in the presence of data autocorrelation. Management Science, 38(8), 1084–1105.
- Wardell, D. G., Moskowitz, H. and Plante, R. D. (1994). Run-length distributions of special-cause control charts for correlated process. Technometrics, 36(1), 3–27.
- West, D., Dellana, S. and Jarrett, J. (2002). Transfer function modelling of processes with dynamic inputs. Journal of Quality Technology, 34(3), 315–326.
- Wilkstrom, C., Albano, C., Eriksson, L., Friden, H., Johansson, E., Nordahl, A., Rannar, S., Sandberg, M., Kettaneh-Wold, N. and Wold, S. (1998). Multivariate process and quality monitoring applied to an electrolysis process: Part II. Multivariate time-series analysis of lagged latent variables. Chemometrics and Intelligent Laboratory Systems, 42(1–2), 233–240.
- Winkel, P. and Zhang, N. F. (2004). Serial correlation of quality control data — on the use of proper control charts. Scandinavian Journal of clinical & Laboratory Investigation, 64(3), 195–203.
- Wisnowski, J. W. and Keats, J. B. (1999). Monitoring the availability of assets with binomial and correlated observations. Quality Engineering, 11(3), 387–393.
- Woodall, W. H., Spitzner, D. J., Montgomery, D. C. and Gupta, S. (2004). Using control charts to monitor process and product quality profiles. Journal of Quality Technology, 36(3), 309–320.
- Wright C. M., Booth, D. E. and Hu, M. Y. (2001). Joint estimation:, SPC method for short-run autocorrelated data. Journal of Quality Technology, 33(3), 365–378.
- Xie, L., Kruger, U., Lieftucht, D., Littler, T., Chen, Q. and Wang, S. (2006). Statistical monitoring of dynamic multivariate processes-Part 1. Modeling autocorrelation and cross-correlation. International Engineering Chemistry Research, 45(5), 1659–1676.
- Yang, J. and Makis, W. (2000). Dynamic response of residuals to external deviations in a controlled production process. Technometrics, 42(3), 290–299.
- Yang, S. and Yang, C. (2005). Effects of imprecise measurement on the two dependent processes control for the autocorrelated observations. The International Journal of Advanced Manufacturing Technology, 26(5–6), 623–630.
- Yang, S. and Yang, C. (2006). An approach to controlling two dependent process steps with autocorrelated. The International Journal of Advanced Manufacturing Technology, 29(1–2), 170–177.
- Yang, K. and Hancock, W. M. (1990). Statistical quality control for correlated samples. International Journal of Production Research, 28(3), 595–608.
- Yashchin, E. (1993). Performance of CUSUM control charts for serially correlated observations. Technometrics, 35(1), 37–52.
- Ye, N., Borror, C. and Mang, Y. B. (2002). EWMA techniques for computer intrusion detection through anomalous changes in event intensity. Quality and Reliability Engineering International, 18(6), 443–451.
- Ye, N., Vilbert, S. and Chen, Q. (2003). Computer intrusion detection through EWMA for autocorrelated and uncorrelated data. IEEE Transaction on Reliability, 52(1), 75–82.
- Zhang (1984). A new type of control charts and a theory of diagnosis with control charts. World Quality Congress Trans. Am. Soc. Qual. Control, 75–85.
- Zhang, N. F. (1997). Detection capability of residual control chart for stationary process data. Journal of Applied Statistics, 24(4), 363–380.
- Zhang, N. F. (1998). A statistical control chart for stationary process data. Technometrics, 40(1), 24–39.
- Zobel, C. W., Cook, D. F. and Nottingham, Q. J. (2004). An augmented neural network classification approach to detecting mean shifts in correlated manufacturing process parameters. International Journal of Production Research, 42(4), 741–758.