REFERENCES
- Bayraktar, E., S. Dayanik, and I. Karatzas. 2006. “The Adaptive Poisson Disorder Problem.” Annals of Applied Probability 16 (3):1190–261.
- Benjamini, Y., and Y. Hochberg. 1995. “Controlling The False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society (B) 57:289–300.
- Chan, H. P. 2016. “Optimal Sequential Detection in Multi-Stream Data.” Annals of Statistics 45 (6):2736–63.
- Goldie, C. M. 1991. “Implicit Renewal Theory and Tails of Solutions of Random Equations.” Annals of Applied Probability 1:126–66.
- Herberts, T., and Y. Jensen. 2004. “Optimal Detection of a Change Point in a Poisson Process for Different Observation Schemes.” Scandinavian Journal of Statistics 31 (3):347–66. doi:https://doi.org/10.1111/j.1467-9469.2004.02-102.x
- Lorden, G., and I. Eisenberg. 1973. “Detection of Failure Rate Increases.” Technometrics 15 (1):167–75. doi:https://doi.org/10.1080/00401706.1973.10489019
- Mei, Y. 2010. “Efficient Scalable Schemes for Monitoring a Large Number of Data Streams.” Biometrika 97 (2):419–33. doi:https://doi.org/10.1093/biomet/asq010
- Moustakides, G. V. 2007. CUSUM Techniques for Sequential Change Detection. Lecture Notes in Probability, Course 8201. New York: Columbia University.
- Ning, W., and Y. Wu. 2021. “Common Change Point Estimation and Changed Panel Isolation after Sequential Detection in an Exponential Family.” Journal of Statistical Theory and Practice 15 (1):1–17. doi:https://doi.org/10.1007/s42519-020-00134-3
- Peskir, G., and A. N. Shiryaev. 2002. “Solving the Poisson Disorder Problem.” In: Advances in Finance and Stochastics: Essays in Honor of Dieter Sonderman, 295–312. New York: Springer.
- Pollak, M. 1987. “Average Run Lengths of an Optimal Method for Detecting a Change in Distribution.” Annals of Statistics 15:749–79.
- Poor, H. V., and O. Hadjiliads. 2009. Quickest Detection. Cambridge: Cambridge University Press.
- Rabinovich, M., A. Ramdas, M. I. Jordan, and M. J. Wainwright. 2020. “Optimal Rates and Tradeoffs in Multiple Testing.” Statistica Sinica 30:741–62. doi:https://doi.org/10.5705/ss.202017.0468
- Shao, X., and W. B. Wu. 2004. “Limit Theorems for Iterated Random Functions.” Journal of Applied Probability 41 (2):425–36. doi:https://doi.org/10.1239/jap/1082999076
- Siegmund, D. 1985. Sequential Analysis: Tests and Confidence Intervals. New York: Springer.
- Tartakovsky, A. G., and V. V. Veeravalli. 2008. “Asymptotically Optimal Quickest Detection Change Detection in Distributed Sensor.” Sequential Analysis 27 (4):441–75. doi:https://doi.org/10.1080/07474940802446236
- Wu, W. B. 2008. “On False Discovery Control under Dependence.” Annals of Statistics 36 (1):364–80.
- Wu, Y. 2005. Inference for Change-Point and Post-Change Means after a CUSUM Test. Lecture Notes in Statistics, vol. 180, New York: Springer.
- Wu, Y. 2019. “A Combined SR-CUSUM Procedure for Detecting Common Changes in Panel Data.” Communications in Statistics – Theory and Methods 48 (17):4302–19. doi:https://doi.org/10.1080/03610926.2018.1494285
- Wu, Y. 2020. “Estimation of Common Change Point and Isolation of Changed Panels after Sequential Detection.” Sequential Analysis 39 (1):52–64. doi:https://doi.org/10.1080/07474946.2020.1726685
- Xie, Y., and D. Siegmund. 2013. “Sequential Multi-Sensor Change-Point Detection.” Annals of Statistics 41:670–92.