References
- M. Bagshaw and R.A. Johnson, The influence of reference values and estimated variance on the ARL of CUSUM tests, J. R. Stat. Soc. Ser. B Methodol. 37 (1975), pp. 413–420.
- R.F. Barber and E.J. Candès, Controlling the false discovery rate via knockoffs, Ann. Stat. 43 (2015), pp. 2055–2085.
- R.F. Barber and E.J. Candès, A knockoff filter for high-dimensional selective inference, Ann. Stat. 47 (2019), pp. 2504–2537.
- S. Bates, E. Candès, L. Janson, and W. Wang, Metropolized knockoff sampling, J. Am. Stat. Assoc. 116 (2021), pp. 1413–1427.
- Y. Benjamini, A.M. Krieger, and D. Yekutieli, Adaptive linear step-up procedures that control the false discovery rate, Biometrika 93 (2006), pp. 491–507.
- G. Capizzi and G. Masarotto, A least angle regression control chart for multidimensional data, Technometrics 53 (2011), pp. 285–296.
- L. Du and C. Zou, On-line control of false discovery rates for multiple datastreams, J. Stat. Plan. Inference. 194 (2018), pp. 1–14.
- C.-K. Ing, T.-L. Lai, M. Shen, K. Tsang, and S.-H. Yu, Multiple testing in regression models with applications to fault diagnosis in the big data era, Technometrics 59 (2017), pp. 351–360.
- W. Jiang, K. Wang, and F. Tsung, A variable-selection-based multivariate EWMA chart for process monitoring and diagnosis, J. Qual. Technol. 44 (2012), pp. 209–230.
- Y. Li and F. Tsung, False discovery rate-adjusted charting schemes for multistage process monitoring and fault identification, Technometrics 51 (2009), pp. 186–205.
- J. Li, J. Jin, and J. Shi, Causation-based t2 decomposition for multivariate process monitoring and diagnosis, J. Qual. Technol. 40 (2008), pp. 46–58.
- W. Li, X. Pu, F. Tsung, and D. Xiang, A robust self-starting spatial rank multivariate EWMA chart based on forward variable selection, Comput. Ind. Eng. 103 (2017), pp. 116–130.
- W. Li, D. Xiang, F. Tsung, and X. Pu, A diagnostic procedure for high-dimensional data streams via missed discovery rate control, Technometrics 62 (2020), pp. 84–100.
- W. Liang, D. Xiang, and X. Pu, A robust multivariate EWMA control chart for detecting sparse mean shifts, J. Qual. Technol. 48 (2016), pp. 265–283.
- R.L. Mason, N.D. Tracy, and J.C. Young, Decomposition of t2 for multivariate control chart interpretation, J. Qual. Technol. 27 (1995), pp. 99–108.
- R.L. Mason, N.D. Tracy, and J.C. Young, A practical approach for interpreting multivariate t2 control chart signals, J. Qual. Technol. 29 (1997), pp. 396–406.
- Y. Mei, Quickest detection in censoring sensor networks, in 2011 IEEE International Symposium on Information Theory Proceedings, IEEE 2011, Silver Spring, MD, pp. 2148–2152.
- K. Wang and W. Jiang, High-dimensional process monitoring and fault isolation via variable selection, J. Qual. Technol. 41 (2009), pp. 247–258.
- W.H. Woodall and D.C. Montgomery, Some current directions in the theory and application of statistical process monitoring, J. Qual. Technol. 46 (2014), pp. 78–94.
- X. Xian, A. Wang, and K. Liu, A nonparametric adaptive sampling strategy for online monitoring of big data streams, Technometrics 60 (2018), pp. 14–25.
- K.D. Zamba and D.M. Hawkins, A multivariate change-point model for statistical process control, Technometrics 48 (2006), pp. 539–549.
- Y. Zhu and W. Jiang, An adaptive t2 chart for multivariate process monitoring and diagnosis, IIE Trans. 41 (2009), pp. 1007–1018.
- C. Zou, Z. Wang, X. Zi, and W. Jiang, An efficient online monitoring method for high-dimensional data streams, Technometrics 57 (2015), pp. 374–387.