CUSUM multi-chart based on nonparametric likelihood approach for detecting unknown abrupt changes and its application for network data

References

• Lai TL. Sequential change-point detection in quality control and dynamic systems. J R Stat Soc Ser B. 1995;57:613–658.
   Google Scholar
• Montgomery DC. Introduction to statistical quality control. 3rd ed. New York: Wiley; 1996.
   Google Scholar
• Qiu P. Introduction to statistical process control. Boca Raton (FL): Chapman & Hall/CRC; 2014.
   Google Scholar
• Siegmund D, Venkatraman ES. Using the generalized likelihood ratio statistic for sequential detection of a change-point. Ann Statist. 1995;23:255–271.
   Web of Science ®Google Scholar
• Han D, Tsung F. A generalized EWMA control chart and its comparison with the optimal EWMA, CUSUM and GLR schemes. Ann Statist. 2004;32:316–339.
   Web of Science ®Google Scholar
• Lorden G. Procedures for reacting to a change in distribution. Ann Math Statist. 1971;42:1897–1908.
   Google Scholar
• Lucas JM. Combined Schewhart-CUSUM quality control schame. J Qual Technol. 1982;14:51–59.
   Web of Science ®Google Scholar
• Sparks RS. CUSUM charts for signalling varying location shifts. J Qual Technol. 2000;32:157–171.
   Web of Science ®Google Scholar
• Han D, Tsung F, Hu X, et al. CUSUM and EWMA multi-charts for detection a range of mean shifts. Statist Sinica. 2007;17:1139–1164.
   Web of Science ®Google Scholar
• Geng J, Erhan B, Lai L. Multi-chart detection procedure for Bayesian quickest change-point detection with unknown post-change parameters. 2017. Available from: arxiv:1708.06901.
   Google Scholar
• Bakir ST, Reynolds MR. A nonparametric procedure for process control based on within-group ranking. Technometrics. 1979;21(2):175–183.
   Web of Science ®Google Scholar
• McDonald D. A CUSUM procedure based on sequential ranks. Naval Res Logist. 1990;37(5):627–646.
   Web of Science ®Google Scholar
• Hackl P, Ledolter J. A control chart based on ranks. J Qual Technol. 1991;23(2):117–124.
   Web of Science ®Google Scholar
• Zhou C, Zou C, Zhang Y, et al. Nonparametric control chart based on change-point model. Statist Papers. 2009;50:13–28.
   Web of Science ®Google Scholar
• Zou C, Tsung F. Likelihood ratio-based distribution-free EWMA control charts. J Qual Technol. 2010;42(2):174–196.
   Web of Science ®Google Scholar
• Wang D, Zhang L, Xiong Q. A nonparametric CUSUM control chart based on the Mann-Whitney statistic. Comm Statist Theory Methods. 2017;46:4713–4725.
   Web of Science ®Google Scholar
• Liu R. Control charts for multivariate processes. J Am Stat Assoc. 1995;90(432):1380–1387.
   Web of Science ®Google Scholar
• Qiu P, Hawkins DM. A rank-based multivariate CUSUM procedure. Technometrics. 2001;43(2):120–132.
   Web of Science ®Google Scholar
• Qiu P. Distribution-free multivariate process control based on log-linear modeling. IIE Trans. 2008;40(7):664–677.
   Web of Science ®Google Scholar
• Zou C, Tsung F. A multivariate sign EWMA control chart. Technometrics. 2011;53(1):84–97.
   Web of Science ®Google Scholar
• Zou C, Wang Z, Tsung F. A spatial rank-based multivariate EWMA control chart. Naval Res Logist. 2012;59(2):91–110.
   Web of Science ®Google Scholar
• Albers W, Kallenberg WCM. Empirical nonparametric control charts: estimation effects and corrections. J Appl Stat. 2004;31(3):345–360.
   Web of Science ®Google Scholar
• Jones LA, Jordan V, Champ CW. Distribution-free phase I control charts for subgroup location. J Qual Technol. 2009;41(3):304–316.
   Web of Science ®Google Scholar
• Lombard F, Van Zyl C. Signed sequential rank CUSUMs. Comput Statist Data Anal. 2018;118(1):30–39.
   Web of Science ®Google Scholar
• Padila OHM, Yu Y, Wang D, et al. Optimal nonparametric change point detection and localization. 2019. Avaliable from: arxiv:1905.10019.
   Google Scholar
• Li J. Nonparametric adaptive CUSUM chart for detecting arbitrary distributional changes. J Qual Technol.2021;53(2):154-172.
   Web of Science ®Google Scholar
• Zhang J. Powerful goodness-of-fit tests based on the likelihood ratio. J R Stat Soc Ser B Stat Methodol. 2002;64:281–294.
   Web of Science ®Google Scholar
• Zhang J. Powerful two-sample tests based on the likelihood ratio. Technometrics. 2006;48:95–103.
   Web of Science ®Google Scholar
• Zou C, Yin G, Feng L, et al. Nonparametric maximum likelihood approach to multiple change-point problems. Ann Statist. 2014;42:970–1002.
   Web of Science ®Google Scholar
• Han D, Tsung F. Detection and diagnosis of unknown abrupt changes using CUSUM multi-chart schemes. Seq Anal. 2007;26(3):225–249.
   Google Scholar
• Siegmund D. Sequential analysis: tests and confidence intervals. New York: Springer-Verlag; 1985.
   Google Scholar
• Han D, Tsung F, Li Y, et al. Detection of changes in a random financial sequence with a stable distribution. J Appl Stat. 2010;37(7):1089–1111.
   Web of Science ®Google Scholar
• Donoho DL, Johnstone IM. Ideal spatial adaptation by wavelet shrinkage. Biometrika. 1994;3(3):425–455.
   Web of Science ®Google Scholar
• Krapivsky PL, Rodgers GJ, Redner S. Degree distributions of growing networks. Phys Rev Lett. 2001;86(23):5401–5404.
   PubMed Web of Science ®Google Scholar
• Barabäsi AL, Bonabeau E. Scale-free networks. Sci Amer. 2003;288(5):60–69.
   PubMed Web of Science ®Google Scholar
• Van Mieghem P, Omic J, Kooij R. Virus spread in networks. IEEE ACM Trans Netw. 2009 Feb;17(1):1–14.
   Web of Science ®Google Scholar
• Restrepo JG, Ott E, Hunt BR. Onset of synchronization in large networks of coupled oscillators. Phys Rev E. 2005;71:Article ID 036151, 1–12.
   Web of Science ®Google Scholar
• Mieghem P. Graph spectra for complex networks. Cambridge University Press,Cambridge, New York; 2011.
   Google Scholar