References
- Arellano, M. S., and S. Bond. 1991. Some tests of specification for panel data: monte carlo evidence and an application to employment equations. The Review of Economic Studies 58 (2):277. doi:https://doi.org/10.2307/2297968.
- Atashgar, K., and N. Rafiee. 2019. Identification of the change point in panel data using simultaneously EWMAA and CUSUM. Advances in Industrial Engineering 52 (4):471–668.
- Bai, J. 2010. Common breaks in means and variances for panel data. Journal of Econometrics 157 (1):78–92. doi:https://doi.org/10.1016/j.jeconom.2009.10.020.
- Chen, Z., and Y. Hu. 2017. Cumulative sum estimator for change-point in panel data. Statistical Papers 58 (3):707–28. doi:https://doi.org/10.1007/s00362-015-0722-y.
- Cho, H. 2016. Change-point detection in panel data via double CUSUM statistic. Electronic Journal of Statistics 10 (2):2000–38. doi:https://doi.org/10.1214/16-EJS1155.
- Cho, H., and P. Fryzlewicz. 2012. Multiscale and multilevel technique for consistent segmentation of nonstationary time series. Statistica Sinica 22 (1):207–29. doi:https://doi.org/10.5705/ss.2009.280.
- Cho, H., and P. Fryzlewicz. 2015. Multiple-change-point detection for high dimensional time series via sparsified binary segmentation. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 77 (2):475–507. doi:https://doi.org/10.1111/rssb.12079.
- De Wachter, S., and E. Tzavalis. 2012. Detection of structural breaks in linear dynamic panel data models. Computational Statistics & Data Analysis 56 (11):3020–34. doi:https://doi.org/10.1016/j.csda.2012.02.025.
- Enikeeva, F., and Z. Harchaoui. 2014. High-dimensional change-point detection with sparse alternatives. The Annals of Statistics 47 (4):2051–79.
- Enomoto, T., and Y. Nagata. 2016. Detection of change points in panel data based on bayesian MT method. Total Quality Science 2 (1):36–47. doi:https://doi.org/10.17929/tqs.2.36.
- Horváth, L., and M. Hušková. 2012. Change-point detection in panel data. Journal of Time Series Analysis 33 (4):631–48. doi:https://doi.org/10.1111/j.1467-9892.2012.00796.x.
- Jirak, M. 2015. Uniform change point tests in high dimension. The Annals of Statistics 43 (6):2451–83. doi:https://doi.org/10.1214/15-AOS1347.
- Joseph, L., and D. B. Wolfson. 1992. Estimation in multi-path change-point problems. Communications in Statistics - Theory and Methods 21 (4):897–913. doi:https://doi.org/10.1080/03610929208830822.
- Joseph, L., and D. B. Wolfson. 1993. Maximum likelihood estimation in the multi-path change point problem. Annals of the Institute of Statistical Mathematics 45 (3):511–30. doi:https://doi.org/10.1007/BF00773352.
- Khan, N., M. Aslam, C. Jun, and J. Hussain. 2018. Design of acceptance sampling plan using a modified EWMA statistic. Communications in Statistics - Theory and Methods 47 (12):2881–91. doi:https://doi.org/10.1080/03610926.2017.1343846.
- Li, F., Z. Tian, Y. Xiao, and Z. Chen. 2015. Variance change-point detection in panel data models. Economics Letters 126:140–3. doi:https://doi.org/10.1016/j.econlet.2014.12.005.
- Maciak, M.,. B. Peštová, and M. Pešta. 2018. Structural breaks in dependent, heteroscedastic, and extremal panel data. Kybernetika 54 (6):1106–21.
- Montgomery, D. C. 2007. Introduction to statistical quality control. USA: John Wiley & Sons.
- Patel, A. K., and J. Divecha. 2011. Modified exponentially weighted moving average (EWMA) control chart for an analytical process data. Journal of Chemical Engineering and Materials Science 2 (1):12–20.
- Peštová, B., and M. Pešta. 2015. Testing structural changes in panel data with small fixed panel size and bootstrap. Metrika 78 (6):665–89. doi:https://doi.org/10.1007/s00184-014-0522-8.
- Peštová, B., and M. Pešta. 2017. Change point estimation in panel data without boundary issue. Risks 5 (1):7. doi:https://doi.org/10.3390/risks5010007.
- Roberts, S. 1959. Control chart tests based on geometric moving averages. Technometrics 1 (3):239–50. doi:https://doi.org/10.1080/00401706.1959.10489860.
- Sen, A., and M. S. Srivastava. 1975. On tests for detecting a change in mean. The Annals of Statistics 3 (1):98–108. doi:https://doi.org/10.1214/aos/1176343001.
- Taguchi, G. 2002. Technological development in the MT system. Japan Standards Association. (In Japanese)
- Venkatraman, E. S. 1992. Consistency results in multiple change-point problems. Technical Report No. 24, Department of Statistics, Stanford University.
- Vostrikova, L. J. 1981. Detecting ‘disorder’ in multidimensional random processes. Soviet Doklady Mathematics 24:55–9.
- Zhang, N. R., D. O. Siegmund, H. Ji, and J. Z. Li. 2010. Detecting simultaneous change points in multiple sequences. Biometrika 97 (3):631–45. doi:https://doi.org/10.1093/biomet/asq025.
- Zhu, X., Y. Li, C. Liang, J. Chen, and D. Wu. 2013. Copula based change point detection for financial contagion in chinese banking. Information Technology and Quantitative Management (ITQM) 17:619–26. doi:https://doi.org/10.1016/j.procs.2013.05.080.