Phase II control charts for autocorrelated processes

References

• Aitkin, M. (1997). The calibration of P-values, posterior Bayes factors and the AIC from the posterior distribution of the likelihood (with discussion). Statistics and Computing, 7, 253–272. doi:10.1023/A:1018550505678.
   Web of Science ®Google Scholar
• Aitkin, M., Boys, R.J., & Chadwick, T. (2005). Bayesian point null hypothesis testing via the posterior likelihood ratio. Statistics and Computing, 15, 217–230. doi:10.1007/s11222-005-1310-0.
   Web of Science ®Google Scholar
• Alwan, L.C., & Roberts, H.V. (1988). Time-series modeling for statistical process control. Journal of Business & Economic Statistics, 6, 87–95. doi:10.2307/1391421.
   Web of Science ®Google Scholar
• Bagshaw, M., & Johnson, R.A. (1975). The effect of serial correlation on the performance of CUSUM tests II. Technometrics, 17, 73–80. doi:10.1080/00401706.1975.10489274.
   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, 517–543.
   Web of Science ®Google Scholar
• Box, G.E.P., Luceño, A., & Paniagua-Quinones, M.C. (2009). Statistical control by monitoring and adjustment (2nd ed.). New York: Wiley.
   Google Scholar
• Durbin, J., & Koopman, S.J. (2012). Time series analysis by state space methods (2nd ed.). Oxford: Oxford University Press.
   Google Scholar
• Frisen, M. (2007). Financial surveillance. New York: Wiley.
   Google Scholar
• Hawkins, D.M. (1991). Multivariate quality control based on regression-adjusted variables. Technometrics, 33, 61–75. doi:10.2307/1269008.
   Web of Science ®Google Scholar
• Jeffreys, H. (1935). Some tests of significance, treated by the theory of probability. Proceedings of the Cambridge Philosophy Society, 31, 203–222. doi:10.1017/S030500410001330X.
   Google Scholar
• Jeffreys, H. (1961). Theory of probability (3rd ed.). Oxford: Oxford University Press.
   Google Scholar
• Johnson, R.A., & Bagshaw, M. (1974). The effect of serial correlation on the performance of CUSUM tests. Technometrics, 16, 103–112. doi:10.1080/00401706.1975.10489274.
   Web of Science ®Google Scholar
• Karvounidis, T., Chimos, K., Bersimis, S., & Douligeris, C. (2012). An integrated self-evaluated framework for embedding Web 2.0 technologies in the educational process. Proceedings of the IEEE Global Engineering Education Conference, Morocco, Marrakesh.
   Google Scholar
• Kass, R.E., & Raftery, A.E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773–795. doi:10.1080/01621459.1995.10476572.
   Web of Science ®Google Scholar
• Kim, S.B., Jitpitaklert, W., Park, S.K., & Hwang, S.J. (2012). Data mining model-based control charts for multivariate and autocorrelated processes. Expert Systems with Applications, 39, 2073–2081. doi:10.1016/j.eswa.2011.08.010.
   Web of Science ®Google Scholar
• Lund, R., & Seymour, L. (1999). Assessing temperature anomalies for a geographical region: A control chart approach. Environmetrics, 8, 469–483. doi: 10.1002/(SICI)1099-095X(199903/04)10:2<163::AID-ENV345>3.0.CO;2-L.
   Google Scholar
• Montgomery, D.C., & Mastrangelo, C.M. (1991). Some statistical process control methods for autocorrelated data. Journal of Quality Technology, 23, 179–204.
   Web of Science ®Google Scholar
• Pan, J.N., & Chen, S.T. (2008). Monitoring long-memory air quality data using ARFIMA model. Environmetrics, 19, 209–219. doi:10.1002/env.882.
   Web of Science ®Google Scholar
• Pan, X., & Jarrett, J. (2004). Applying state space to SPC: Monitoring multivariate time series. Journal of Applied Statistics, 31, 397–418. doi:10.1080/02664760410001681701.
   Web of Science ®Google Scholar
• Psarakis, S., & Papaleonida, G.E.A. (2007). SPC procedures for monitoring autocorrelated processes. Quality Technology and Quantitative Management, 4, 501–540. doi:10.1080/16843703.2007.11673168.
   Google Scholar
• Robert, C.P. (2007). The Bayesian choice: From decision-theoretic foundations to computational implementation (2nd ed.). New York: Springer.
   Google Scholar
• Schioler, L., & Frisen, M. (2012). Multivariate outbreak detection. Journal of Applied Statistics, 39, 223–242. doi:10.1080/02664763.2011.584522.
   Web of Science ®Google Scholar
• Schmid, W. (1997). On EWMA charts for time series. Frontiers in Statistical Quality Control, 5, 115–137. doi:10.1007/978-3-642-59239-3_10.
   Google Scholar
• Shmueli, G., & Burkom, H. (2010). Statistical challenges facing early outbreak detection in biosurveillance. Technometrics, 52, 39–51. doi:10.1198/TECH.2010.06134.
   Web of Science ®Google Scholar
• Triantafyllopoulos, K. (2006). Multivariate control charts based on Bayesian state space models. Quality and Reliability Engineering International, 22, 693–707. doi:10.1002/qre.807.
   Web of Science ®Google Scholar
• Triantafyllopoulos, K., & Nason, G.P. (2007). A Bayesian analysis of moving average processes with time-varying parameters. Computational Statistics and Data Analysis, 52, 1025–1046.
   Web of Science ®Google Scholar
• Van Brackle, L.N., & Reynolds, M.R. (1997). EWMA and CUSUM control charts in the presence of autocorrelation. Communications in Statistics: Simulation and Computation, 26, 979–1008. doi:10.1080/03610919708813421.
   Web of Science ®Google Scholar
• West, M. (1986). Bayesian model monitoring. Journal of the Royal Statistical Society Series B, 48, 70–78. doi:0035-9246/86/48070.
   Google Scholar
• West, M., & Harrison, P.J. (1997). Bayesian forecasting and dynamic models (2nd ed.). New York: Springer.
   Google Scholar
• Woodall, W.H. (2006). The use of control charts in healthcare and public health surveillance (with discussion). Journal of Quality Technology, 38, 89–104.
   Web of Science ®Google Scholar
• Woodall, W.H., & Montgomery, D.C. (1999). Research issues and ideas in statistical process control. Journal of Quality Technology, 31, 376–386. doi:10/1999; 31(4).
   Web of Science ®Google Scholar
• Ye, N., & Chen, Q. (2001). An anomaly detection technique based on a chi-squared statistic for detecting intrusions into information systems. Quality and Reliability Engineering International, 17, 105–112. doi:10.1002/qre.392.
   Web of Science ®Google Scholar
• Zhang, N.F., & Pollard, J.P. (1994). Analysis of autocorrelations in dynamic processes. Technometrics, 36, 354–368. doi:10.2307/1269951.
   Web of Science ®Google Scholar