A two-stage monitoring scheme for multiple high-dimensional data streams

References

• Kamran P, Peihua Q, Changliang Z. A change-point approach for phase-i analysis in multivariate profile monitoring and diagnosis. Technometrics. 2016;58(2):191–204. doi: 10.1080/00401706.2015.1042168
   Web of Science ®Google Scholar
• Changliang Z, Zhaojun W, Xuemin Z, et al. An efficient online monitoring method for high-dimensional data streams. Technometrics. 2015;57(3):374–387. doi: 10.1080/00401706.2014.940089
   Web of Science ®Google Scholar
• Hao Y, Kamran P, Jianjun S. Real-time monitoring of high-dimensional functional data streams via spatio-temporal smooth sparse decomposition. Technometrics. 2018;60(2):181–197. doi: 10.1080/00401706.2017.1346522
   Web of Science ®Google Scholar
• Jian L, Rui L, Changshui Z, et al. Poster paper: predicting seizures from electroencephalography recordings: a knowledge transfer strategy. In IEEE International Conference on Healthcare Informatics, 2016.
   Google Scholar
• Ana MEG, Dan L, Kamran P. An adaptive sampling strategy for online monitoring and diagnosis of high-dimensional streaming data. Technometrics. 2021;64(2):253–269.
   Web of Science ®Google Scholar
• Samaneh E, Chitta R, Kamran P. Monitoring and root-cause diagnostics of high-dimensional data streams. J Qual Technol. 2022;54(1):20–43. doi: 10.1080/00224065.2020.1805377
   Web of Science ®Google Scholar
• Dequan Q, Zhonghua L, Zhaojun W. On-line monitoring data quality of high-dimensional data streams. J Stat Comput Simul. 2016;86(11):2204–2216. doi: 10.1080/00949655.2015.1106542
   Web of Science ®Google Scholar
• Rehan AKSFM, Muhammad AR. A new exponentially weighted moving average control chart to monitor count data with applications in healthcare and manufacturing. J Stat Comput Simul. 2023;93(18):3308–3328. doi: 10.1080/00949655.2023.2220859
   Web of Science ®Google Scholar
• Kaibo L, Yajun M, Jianjun S. An adaptive sampling strategy for online high-dimensional process monitoring. Technometrics. 2015;57(3):305–319. doi: 10.1080/00401706.2014.947005
   Web of Science ®Google Scholar
• Long F, Haojie R, Changliang Z. A setwise EWMA scheme for monitoring high-dimensional datastreams. Random Matrices: Theory Appl. 2020;09(02):2050004. doi: 10.1142/S2010326320500045
   Google Scholar
• Jun L. A two-stage online monitoring procedure for high-dimensional data streams. J Qual Technol. 2019;51(4):392–406. doi: 10.1080/00224065.2018.1507562
   Web of Science ®Google Scholar
• Jun L. Efficient global monitoring statistics for high-dimensional data. Qual Reliab Eng Int. 2020;36(1):18–32. doi: 10.1002/qre.v36.1
   Web of Science ®Google Scholar
• Lilun D, Changliang Z. On-line control of false discovery rates for multiple datastreams. J Stat Plan Inference. 2018;194:1–14. doi: 10.1016/j.jspi.2017.10.006
   Web of Science ®Google Scholar
• Xiaochen X, Chen Z, Scott B, et al. Online monitoring of big data streams: a rank-based sampling algorithm by data augmentation. J Qual Technol. 2021;53(2):135–153. doi: 10.1080/00224065.2019.1681924
   Web of Science ®Google Scholar
• Tingshan H, Nagarajan K, Harish S, et al. An efficient strategy for online performance monitoring of datacenters via adaptive sampling. IEEE Trans Cloud Comput. 2019;7(1):155–169. doi: 10.1109/TCC.2016.2603473
   Web of Science ®Google Scholar
• Grigg OA, Spiegelhalter DJ. An empirical approximation to the null unbounded steady-state distribution of the cumulative sum statistic. Technometrics. 2008;50(4):501–511. doi: 10.1198/004017008000000299
   Web of Science ®Google Scholar
• Yuan W, Yajun M. Large-scale multi-stream quickest change detection via shrinkage post-change estimation. IEEE Trans Inform Theory. 2015;61(12):6926–6938. doi: 10.1109/TIT.2015.2495361
   Web of Science ®Google Scholar
• Myong K, Jeong Sangahn K, Elsayed EA. A penalized likelihood-based quality monitoring via l2-norm regularization for high-dimensional processes. J Qual Technol. 2020;52(3):265–280. doi: 10.1080/00224065.2019.1571348
   Web of Science ®Google Scholar
• Zhang R, Mei Y, Shi J. Robust change detection for large-scale data streams. Seq Anal. 2022;41(1):1–19. doi: 10.1080/07474946.2022.2043045
   Web of Science ®Google Scholar
• Zhang Y, Wang R, Shao X. Adaptive inference for change points in high-dimensional data. J Am Stat Assoc. 2022;117(540):1751–1762. doi: 10.1080/01621459.2021.1884562
   Web of Science ®Google Scholar
• Lanning JW, Montgomery DC, Runger GC. Lanning and George C. Runger monitoring a multiple stream filling operation using fractional samples. Qual Eng. 2002;15(2):183–195. doi: 10.1081/QEN-120015851
   Google Scholar
• Liu X, MacKay RJ, Steiner SH. Monitoring multiple stream processes. Qual Eng. 2008;20(3):296–308. doi: 10.1080/08982110802035404
   Google Scholar
• Ahmadi-Javid A, Ebadi M. A two-step method for monitoring normally distributed multi-stream processes in high dimensions. Qual Eng. 2020;33(1):143–155. doi: 10.1080/08982112.2020.1786118
   Web of Science ®Google Scholar
• Zou C, Tseng ST, Wang Z. Outlier detection in general profiles using penalized regression method. IEEE Trans. 2014;46(2):106–117. doi: 10.1080/0740817X.2012.762486
   Web of Science ®Google Scholar
• Barnett V, Lewis T. Outliers in statistical data. 3rd ed. New York: John Wiley and Sons; 1994.
   Google Scholar
• Hubert M. Multivariate outlier detection and robust covariance matrix estimation – discussion. Technometrics. 2001;43(3):303–306.
   Google Scholar
• Cai TT, Xia Y. High-dimensional sparse MANOVA. J Multivar Anal. 2014;131:174–196. doi: 10.1016/j.jmva.2014.07.002
   Web of Science ®Google Scholar
• Rocco B. Another characterization of the type-I extreme value distribution. Stat Probab Lett. 1987;5(2):83–85. doi: 10.1016/0167-7152(87)90060-5
   Web of Science ®Google Scholar
• Singh VP. Extreme value type-I distribution. Dordrecht: Springer; 1998. p. 108–136.
   Google Scholar
• Zhang H, Albin S. Detecting outliers in complex profiles using a χ2 control chart method. IIE Trans. 2009;41(4):335–345. doi: 10.1080/07408170802323000
   Web of Science ®Google Scholar
• Efron B, Tibshirani RJ. An introduction to the bootstrap. monographs on statistics and applied probability. Vol. 57. London: Chapman and Hall; 1993. p. 436.
   Google Scholar
• Kurt MN, Yilmaz Y, Wang X. Real-time nonparametric anomaly detection in high-dimensional settings. IEEE Trans Pattern Anal Mach Intell. 2021;43(7):2463–2479.
   PubMed Web of Science ®Google Scholar
• Li W, Xiang D, Tsung F, et al. A diagnostic procedure for high-dimensional data streams via missed discovery rate control. Technometrics. 2020;62(1):84–100. doi: 10.1080/00401706.2019.1575284
   Web of Science ®Google Scholar
• Xiang D, Qiu P, Wang D, et al. Reliable post-signal fault diagnosis for correlated high-dimensional data streams. Technometrics. 2022;64(3):323–334. doi: 10.1080/00401706.2021.1979100
   Web of Science ®Google Scholar