A unified diagnostic framework via symmetrized data aggregation

References

• Benjamini, Y. and Heller, R. (2007) False discovery rates for spatial signals. Journal of the American Statistical Association, 102(480), 1272–1281.
   Web of Science ®Google Scholar
• Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 57(1), 289–300.
   Web of Science ®Google Scholar
• Bickel, P.J. and Levina, E. (2008) Covariance regularization by thresholding. The Annals of Statistics, 36(6), 2577–2604.
   Web of Science ®Google Scholar
• Bühlmann, P. and Mandozzi, J. (2014) High-dimensional variable screening and bias in subsequent inference, with an empirical comparison. Computational Statistics, 29(3), 407–430.
   Web of Science ®Google Scholar
• Capizzi, G. and Masarotto, G. (2011) A least angle regression control chart for multidimensional data. Technometrics, 53(3), 285–296.
   Web of Science ®Google Scholar
• Cho, H. and Fryzlewicz, P. (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.
   Web of Science ®Google Scholar
• Delaigle, A., Hall, P. and Jin, J. (2011) Robustness and accuracy of methods for high dimensional data analysis based on student’s t-statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(3), 283–301.
   Google Scholar
• Du, L., Guo, X., Sun, W. and Zou, C. (2021) False discovery rate control under general dependence by symmetrized data aggregation. Journal of the American Statistical Association, 118(541), 607–621.
   Web of Science ®Google Scholar
• Du, L. and Zou, C. (2018) On-line control of false discovery rates for multiple datastreams. Journal of Statistical Planning and Inference, 194, 1–14.
   Web of Science ®Google Scholar
• Efron, B. (2004) Large-scale simultaneous hypothesis testing: The choice of a null hypothesis. Journal of the American Statistical Association, 99(465), 96–104.
   Web of Science ®Google Scholar
• Fan, J. and Lv, J. (2008) Sure independence screening for ultrahigh dimensional feature space. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70(5), 849–911.
   PubMed Web of Science ®Google Scholar
• Feng, L., Ren, H. and Zou, C. (2020) A setwise EWMA scheme for monitoring high-dimensional datastreams. Random Matrices: Theory and Applications, 9(02), 2050004.
   Google Scholar
• Grout, I.A. (2005) Integrated Circuit Test Engineering: Modern Techniques, Springer, New York.
   Google Scholar
• Hall, P. and Jin, J. (2010) Innovated higher criticism for detecting sparse signals in correlated noise. The Annals of Statistics, 38(3), 1686–1732.
   Web of Science ®Google Scholar
• Higham, N.J. (2002) Computing the nearest correlation matrix—a problem from finance. IMA Journal of Numerical Analysis, 22(3), 329–343.
   Web of Science ®Google Scholar
• Huang, C., Farewell, D. and Pan, J. (2017) A calibration method for non-positive definite covariance matrix in multivariate data analysis. Journal of Multivariate Analysis, 157, 45–52.
   Web of Science ®Google Scholar
• Ing, C.-K., Lai, T.L., Shen, M., Tsang, K. and Yu, S.-H. (2017) Multiple testing in regression models with applications to fault diagnosis in the big data era. Technometrics, 59(3), 351–360.
   Web of Science ®Google Scholar
• Javanmard, A. and Javadi, H. (2019) False discovery rate control via debiased LASSO. Electronic Journal of Statistics, 13(1), 1212–1253.
   Web of Science ®Google Scholar
• Jiang, W., Wang, K. and Tsung, F. (2012) A variable-selection-based multivariate EWMA chart for process monitoring and diagnosis. Journal of Quality Technology, 44(3), 209–230.
   Web of Science ®Google Scholar
• Keener, R.W. (2010) Theoretical Statistics: Topics for A Core Course, Springer, New York.
   Google Scholar
• Lehmann, E.L. and Casella, G. (2006) Theory of Point Estimation, Springer, New York.
   Google Scholar
• Li, J. (2019) A two-stage online monitoring procedure for high-dimensional data streams. Journal of Quality Technology, 51(4), 392–406.
   Web of Science ®Google Scholar
• Li, J., Jin, J. and Shi, J. (2008) Causation-based T2 decomposition for multivariate process monitoring and diagnosis. Journal of Quality Technology, 40(1), 46–58.
   Web of Science ®Google Scholar
• Li, W., Pu, X., Tsung, F. and Xiang, D. (2017) A robust self-starting spatial rank multivariate EWMA chart based on forward variable selection. Computers & Industrial Engineering, 103, 116–130.
   Web of Science ®Google Scholar
• Li, W., Xiang, D., Tsung, F. and Pu, X. (2020) A diagnostic procedure for high-dimensional data streams via missed discovery rate control. Technometrics, 62(1), 84–100.
   Web of Science ®Google Scholar
• Li, Y. and Tsung, F. (2011) Detecting and diagnosing covariance matrix changes in multistage processes. IIE Transactions, 43(4), 259–274.
   Web of Science ®Google Scholar
• Liang, W., Xiang, D. and Pu, X. (2016) A robust multivariate EWMA control chart for detecting sparse mean shifts. Journal of Quality Technology, 48(3), 265–283.
   Web of Science ®Google Scholar
• Liu, W. and Shao, Q.-M. (2014) Phase transition and regularized bootstrap in large-scale t-tests with false discovery rate control. The Annals of Statistics, 42(5), 2003–2025.
   Web of Science ®Google Scholar
• Lockhart, R., Taylor, J., Tibshirani, R.J. and Tibshirani, R. (2014) A significance test for the LASSO. The Annals of Statistics, 42(2), 413.
   PubMed Web of Science ®Google Scholar
• Mason, R.L., Chou, Y.-M. and Young, J.C. (2001) Applying Hotelling’s T2 statistic to batch processes. Journal of Quality Technology, 33(4), 466–479.
   Web of Science ®Google Scholar
• Mason, R.L. and Young, J.C. (2002) Multivariate Statistical Process Control with Industrial Applications. SIAM, Philadelphia, PA.
   Google Scholar
• Meinshausen, N., Meier, L. and Bühlmann, P. (2009) P-values for high-dimensional regression. Journal of the American Statistical Association, 104(488), 1671–1681.
   Web of Science ®Google Scholar
• Neudecker, H. and Wesselman, A.M. (1990) The asymptotic variance matrix of the sample correlation matrix. Linear Algebra and Its Applications, 127, 589–599.
   Web of Science ®Google Scholar
• Paynabar, K., Zou, C. and Qiu, P. (2016) A change-point approach for phase-I analysis in multivariate profile monitoring and diagnosis. Technometrics, 58(2), 191–204.
   Web of Science ®Google Scholar
• Qi, D., Li, Z. and Wang, Z. (2016) On-line monitoring data quality of high-dimensional data streams. Journal of Statistical Computation and Simulation, 86(11), 2204–2216.
   Web of Science ®Google Scholar
• Qiu, P. (2013) Introduction to Statistical Process Control, CRC Press, Boca Raton, FL.
   Google Scholar
• Ren, H., Zou, C., Chen, N. and Li, R. (2020) Large-scale datastreams surveillance via pattern-oriented-sampling. Journal of the American Statistical Association, 117(538), 794–808.
   Web of Science ®Google Scholar
• Sullivan, J.H., Stoumbos, Z.G., Mason, R.L. and Young, J.C. (2007) Step-down analysis for changes in the covariance matrix and other parameters. Journal of Quality Technology, 39(1), 66–84.
   Web of Science ®Google Scholar
• Van De Geer, S.A. and Bühlmann, P. (2009) On the conditions used to prove oracle results for the LASSO. Electronic Journal of Statistics, 3, 1360–1392.
   Web of Science ®Google Scholar
• Venables, W. and Ripley, B. (1999) Graphics, in Modern Applied Statistics with S-PLUS, Springer, New York, pp. 53–92
   Google Scholar
• Wang, K. and Jiang, W. (2009) High-dimensional process monitoring and fault isolation via variable selection. Journal of Quality Technology, 41(3), 247–258.
   Web of Science ®Google Scholar
• Wang, T. and Samworth, R.J. (2018) High dimensional change point estimation via sparse projection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80(1), 57–83.
   Web of Science ®Google Scholar
• Yan, H., Paynabar, K. and Shi, J. (2018) Real-time monitoring of high-dimensional functional data streams via spatio-temporal smooth sparse decomposition. Technometrics, 60(2), 181–197.
   Web of Science ®Google Scholar
• Zhang, Y., Duchi, J.C. and Wainwright, M.J. (2013) Communication-efficient algorithms for statistical optimization. The Journal of Machine Learning Research, 14(1), 3321–3363.
   Google Scholar
• Zhu, Y. and Jiang, W. (2009) An adaptive T2 chart for multivariate process monitoring and diagnosis. IIE Transactions, 41(11), 1007–1018.
   Web of Science ®Google Scholar
• Zou, C., Jiang, W. and Tsung, F. (2011) A LASSO-based diagnostic framework for multivariate statistical process control. Technometrics, 53(3), 297–309.
   Web of Science ®Google Scholar
• Zou, C., Ning, X. and Tsung, F. (2012) LASSO-based multivariate linear profile monitoring. Annals of Operations Research, 192(1), 3–19.
   Web of Science ®Google Scholar
• Zou, C. and Qiu, P. (2009) Multivariate statistical process control using LASSO. Journal of the American Statistical Association, 104(488), 1586–1596.
   Web of Science ®Google Scholar
• Zou, C. and Tsung, F. (2008) Directional MEWMA schemes for multistage process monitoring and diagnosis. Journal of Quality Technology, 40(4), 407–427.
   Web of Science ®Google Scholar
• Zou, C., Tsung, F. and Liu, Y. (2008) A change point approach for phase I analysis in multistage processes. Technometrics, 50(3), 344–356.
   Web of Science ®Google Scholar