References
- Ahmadzadeh, F. 2018. Change point detection with multivariate control charts by artificial neural network. The International Journal of Advanced Manufacturing Technology 97 (9–12):3179–12. doi:10.1007/s00170-009-2193-6.
- Alaeddini, A., M. Ghazanfari, and M. A. Nayeri. 2009. A hybrid fuzzy-statistical clustering approach for estimating the time of changes in fixed and variable sampling control charts. Information Sciences 179 (11):1769–84. doi:10.1016/j.ins.2009.01.019.
- Amiri, A., and S. Allahyari. 2012. Change point estimation methods for control chart postsignal diagnostics: A literature review. Quality and Reliability Engineering International 28 (7):673–85. doi:10.1002/qre.1266.
- Amiri, A., and S. Zolfaghari. 2016. Estimation of change point in two-stage processes subject to step change and linear trend. International Journal of Reliability, Quality and Safety Engineering 23 (02):1650007. doi:10.1142/S0218539316500078.
- Atashgar, K., and R. Noorossana. 2011. An integrating approach to root cause analysis of a bivariate mean vector with a linear trend disturbance. The International Journal of Advanced Manufacturing Technology 52 (1–4):407–20. doi:10.1007/s00170-010-2728-x.
- Ayoubi, M., R. Kazemzadeh, S. Niaki, and A. Amiri. 2014. Change point estimation of a multivariate normal process mean using dynamic linear model. Submitted.
- Ayoubi, M., R. Kazemzadeh, and R. Noorossana. 2014. Estimating multivariate linear profiles change point with a monotonic change in the mean of response variables. The International Journal of Advanced Manufacturing Technology 75 (9–12):1537–56. doi:10.1007/s00170-014-6208-6.
- Ayoubi, M., R. B. Kazemzadeh, and R. Noorossana. 2016. Change point estimation in the mean of multivariate linear profiles with no change type assumption via dynamic linear model. Quality and Reliability Engineering International 32 (2):403–33. doi:10.1002/qre.1760.
- Barone, P. 1987. A method for generating independent realizations of a multivariate normal stationary and invertible ARMA (p, q) process. Journal of Time Series Analysis 8 (2):125–30. doi:10.1111/j.1467-9892.1987.tb00426.x.
- Bodnar, O. 2009. Application of the generalized likelihood ratio test for detecting changes in the mean of multivariate GARCH processes. Communications in Statistics - Simulation and Computation 38 (5):919–38. doi:10.1080/03610910802691861.
- Chang, S. T., and K. P. Lu. 2016. Change‐point detection for shifts in control charts using EM change‐point algorithms. Quality and Reliability Engineering International 32 (3):889–900. doi:10.1002/qre.1800.
- Doǧu, E., and İ. D. Kocakoç. 2011. Estimation of change point in generalized variance control chart. Communications in Statistics - Simulation and Computation 40 (3):345–63. doi:10.1080/03610918.2010.542844.
- Francq, C., R. Roy, and J.-M. Zakoïan. 2005. Diagnostic checking in ARMA models with uncorrelated errors. Journal of the American Statistical Association 100 (470):532–44. doi:10.1198/016214504000001510.
- Ghazanfari, M., A. Alaeddini, S. T. A. Niaki, and M. B. Aryanezhad. 2008. A clustering approach to identify the time of a step change in Shewhart control charts. Quality and Reliability Engineering International 24 (7):765–78. doi:10.1002/qre.925.
- Hawkins, D. M., and K. Zamba. 2005. A change-point model for a shift in variance. Journal of Quality Technology 37 (1):21–31. doi:10.1080/00224065.2005.11980297.
- Jiang, W., K.-L. Tsui, and W. H. Woodall. 2000. A new SPC monitoring method: The ARMA chart. Technometrics 42 (4):399–410. doi:10.1080/00401706.2000.10485713.
- Kazemzadeh, R. B., R. Noorossana, and M. Ayoubi. 2015. Change point estimation of multivariate linear profiles under linear drift. Communications in Statistics - Simulation and Computation 44 (6):1570–99. doi:10.1080/03610918.2013.824093.
- Lee, J., and C. Park. 2007. Estimation of the change point in monitoring the process mean and variance. Communications in Statistics - Simulation and Computation 36 (6):1333–45. doi:10.1080/03610910701569028.
- Li, F., Z. Tian, and P. Qi. 2015. Structural change monitoring for random coefficient autoregressive time series. Communications in Statistics - Simulation and Computation 44 (4):996–1009. doi:10.1080/03610918.2013.800205.
- Ling, S. 2004. Estimation and testing stationarity for double‐autoregressive models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 66 (1):63–78. doi:10.1111/j.1467-9868.2004.00432.x.
- Lu, K.-P., S.-T. Chang, and M.-S. Yang. 2016. Change-point detection for shifts in control charts using fuzzy shift change-point algorithms. Computers & Industrial Engineering 93:12–27. doi:10.1016/j.cie.2015.12.002.
- Malela‐Majika, J. C., and E. Rapoo. 2017. Distribution‐free mixed cumulative sum‐exponentially weighted moving average control charts for detecting mean shifts. Quality and Reliability Engineering International 33 (8):1983–2002. doi:10.1002/qre.2162.
- Meinhold, R. J., and N. D. Singpurwalla. 1983. Understanding the Kalman filter. The American Statistician 37 (2):123–27.
- Montgomery, D. 2009. Introduction to statistical quality control. New York: John Wiley & Sons.
- Nishina, K. 1992. A comparison of control charts from the viewpoint of change‐point estimation. Quality and Reliability Engineering 8 (6):537–41. doi:10.1002/qre.4680080605.
- Noorossana, R., A. Saghaei, K. Paynabar, and S. Abdi. 2009. Identifying the period of a step change in high‐yield processes. Quality and Reliability Engineering International 25 (7):875–83. doi:10.1002/qre.1007.
- Noorossana, R., and A. Shadman. 2009. Estimating the change point of a normal process mean with a monotonic change. Quality and Reliability Engineering International 25 (1):79–90. doi:10.1002/qre.957.
- Page, E. S. 1954. Continuous inspection schemes. Biometrika 41 (1–2):100–15. doi:10.1093/biomet/41.1-2.100.
- Perry, M. B., and J. J. Pignatiello, Jr. 2006. Estimation of the change point of a normal process mean with a linear trend disturbance in SPC. Quality Technology & Quantitative Management 3 (3):325–34. doi:10.1080/16843703.2006.11673118.
- Perry, M. B., J. J. Pignatiello, and J. R. Simpson. 2006. Estimating the change point of a Poisson rate parameter with a linear trend disturbance. Quality and Reliability Engineering International 22 (4):371–84. doi:10.1002/qre.715.
- Perry, M. B., J. J. Pignatiello, and J. R. Simpson. 2007. Estimating the change point of the process fraction non‐conforming with a monotonic change disturbance in spc. Quality and Reliability Engineering International 23 (3):327–39. doi:10.1002/qre.792.
- Petris, G., S. Petrone, and P. Campagnoli. 2007. Dynamic linear models with R. New York: Springer-Verlag.
- Pignatiello, J. J., and T. R. Samuel, Jr. 2001. Estimation of the change point of a normal process mean in SPC applications. Journal of Quality Technology 33 (1):82–95. doi:10.1080/00224065.2001.11980049.
- Safaeipour, A., and S. T. A. Niaki. 2015. Drift change point estimation in multistage processes using MLE. International Journal of Reliability, Quality and Safety Engineering 22 (05):1550025. doi:10.1142/S0218539315500254.
- Samuel, T. R., J. J. Pignatiello, Jr, and J. A. Calvin. 1998a. Identifying the time of a step change in a normal process variance. Quality Engineering 10 (3):529–38. doi:10.1080/08982119808919167.
- Samuel, T. R., J. J. Pignatiello, Jr, and J. A. Calvin. 1998b. Identifying the time of a step change with. Quality Engineering 10 (3):521–27. doi:10.1080/08982119808919166.
- Shao, Q., and L. Yang. 2017. Oracally efficient estimation and consistent model selection for auto‐regressive moving average time series with trend. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 79 (2):507–24. doi:10.1111/rssb.12170.
- Sheikhrabori, R., M. Aminnayeri, and M. Ayoubi. 2018. Maximum likelihood estimation of change point from stationary to nonstationary in autoregressive models using dynamic linear model. Quality and Reliability Engineering International 34 (1):27–36. doi:10.1002/qre.2233.
- Slama, A., and H. Saggou. 2017. A Bayesian analysis of a change in the parameters of autoregressive time series. Communications in Statistics - Simulation and Computation 46 (9):7008–21. doi:10.1080/03610918.2016.1222423.
- Sturludottir, E., H. Gunnlaugsdottir, O. K. Nielsen, and G. Stefansson. 2017. Detection of a changepoint, a mean-shift accompanied with a trend change, in short time-series with autocorrelation. Communications in Statistics - Simulation and Computation 46 (7):5808–18. doi:10.1080/03610918.2014.1002849.
- Tsay, R. S., and G. C. Tiao. 1984. Consistent estimates of autoregressive parameters and extended sample autocorrelation function for stationary and nonstationary ARMA models. Journal of the American Statistical Association 79 (385):84–96. doi:10.1080/01621459.1984.10477068.
- Vollenbröker, B. 2012. Strictly stationary solutions of ARMA equations with fractional noise. Journal of Time Series Analysis 33 (4):570–82. doi:10.1111/j.1467-9892.2012.00788.x.
- Wang, L., G. Libert, and P. Manneback. 1992. Kalman filter algorithm based on singular value decomposition. Proceedings of the 31st IEEE Conference on Decision and Control, 1992, Tucson, AZ, USA.
- Zhang, Y., and X. Li. 1996. Fixed-interval smoothing algorithm based on singular value decomposition. Proceedings of the 1996 IEEE International Conference on Control Applications, 1996, Dearborn, MI, USA.
- Zhu, K., and S. Ling. 2012. Likelihood ratio tests for the structural change of an AR (p) model to a threshold AR (p) model. Journal of Time Series Analysis 33 (2):223–32. doi:10.1111/j.1467-9892.2011.00753.x.