IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/111010.html
   My bibliography  Save this paper

Compound sequential change-point detection in parallel data streams

Author

Listed:
  • Chen, Yunxiao
  • Li, Xiaoou

Abstract

We consider sequential change-point detection in parallel data streams, where each stream has its own change point. Once a change is detected in a data stream, this stream is deactivated permanently. The goal is to maximize the normal operation of the pre-change streams, while controlling the proportion of post-change streams among the active streams at all time points. Taking a Bayesian formulation, we develop a compound decision framework for this problem. A procedure is proposed that is uniformly optimal among all sequential procedures which control the expected proportion of post-change streams at all time points. We also investigate the asymptotic behavior of the proposed method when the number of data streams grows large. Numerical examples are provided to illustrate the use and performance of the proposed method.

Suggested Citation

  • Chen, Yunxiao & Li, Xiaoou, 2023. "Compound sequential change-point detection in parallel data streams," LSE Research Online Documents on Economics 111010, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:111010
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/111010/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wim Linden & Charles Lewis, 2015. "Bayesian Checks on Cheating on Tests," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 689-706, September.
    2. Sun, Wenguang & Cai, T. Tony, 2007. "Oracle and Adaptive Compound Decision Rules for False Discovery Rate Control," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 901-912, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daniel Yekutieli, 2015. "Bayesian tests for composite alternative hypotheses in cross-tabulated data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(2), pages 287-301, June.
    2. Ghosh Debashis, 2012. "Incorporating the Empirical Null Hypothesis into the Benjamini-Hochberg Procedure," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-21, July.
    3. Edison M. Choe & Jinming Zhang & Hua-Hua Chang, 2018. "Sequential Detection of Compromised Items Using Response Times in Computerized Adaptive Testing," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 650-673, September.
    4. Gómez-Villegas Miguel A. & Salazar Isabel & Sanz Luis, 2014. "A Bayesian decision procedure for testing multiple hypotheses in DNA microarray experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(1), pages 49-65, February.
    5. Long Qu & Dan Nettleton & Jack C. M. Dekkers, 2012. "Improved Estimation of the Noncentrality Parameter Distribution from a Large Number of t-Statistics, with Applications to False Discovery Rate Estimation in Microarray Data Analysis," Biometrics, The International Biometric Society, vol. 68(4), pages 1178-1187, December.
    6. Cipolli III, William & Hanson, Timothy & McLain, Alexander C., 2016. "Bayesian nonparametric multiple testing," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 64-79.
    7. Dennis Leung & Wenguang Sun, 2022. "ZAP: Z$$ Z $$‐value adaptive procedures for false discovery rate control with side information," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1886-1946, November.
    8. Lee, Donghwan & Lee, Youngjo, 2016. "Extended likelihood approach to multiple testing with directional error control under a hidden Markov random field model," Journal of Multivariate Analysis, Elsevier, vol. 151(C), pages 1-13.
    9. Hai Shu & Bin Nan & Robert Koeppe, 2015. "Multiple testing for neuroimaging via hidden Markov random field," Biometrics, The International Biometric Society, vol. 71(3), pages 741-750, September.
    10. Sandip Sinharay & Matthew S. Johnson, 2021. "The Use of the Posterior Probability in Score Differencing," Journal of Educational and Behavioral Statistics, , vol. 46(4), pages 403-429, August.
    11. T. Tony Cai & Wenguang Sun & Weinan Wang, 2019. "Covariate‐assisted ranking and screening for large‐scale two‐sample inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 187-234, April.
    12. Zhaoyang Tian & Kun Liang & Pengfei Li, 2021. "A powerful procedure that controls the false discovery rate with directional information," Biometrics, The International Biometric Society, vol. 77(1), pages 212-222, March.
    13. Joungyoun Kim & Donghyeon Yu & Johan Lim & Joong-Ho Won, 2018. "A peeling algorithm for multiple testing on a random field," Computational Statistics, Springer, vol. 33(1), pages 503-525, March.
    14. Jiaying Gu & Roger Koenker, 2017. "Rebayes: an R package for empirical bayes mixture methods," CeMMAP working papers CWP37/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. Chen, Yunxiao & Lu, Yan & Moustaki, Irini, 2022. "Detection of two-way outliers in multivariate data and application to cheating detection in educational tests," LSE Research Online Documents on Economics 112499, London School of Economics and Political Science, LSE Library.
    16. T. Tony Cai & Weidong Liu, 2016. "Large-Scale Multiple Testing of Correlations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 229-240, March.
    17. Wenguang Sun & T. Tony Cai, 2009. "Large‐scale multiple testing under dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 393-424, April.
    18. Saharon Rosset & Ruth Heller & Amichai Painsky & Ehud Aharoni, 2022. "Optimal and maximin procedures for multiple testing problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1105-1128, September.
    19. Joshua Habiger & Edsel Peña, 2011. "Randomised -values and nonparametric procedures in multiple testing," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(3), pages 583-604.
    20. Ni, Huangjing & Qin, Jiaolong & Zhou, Luping & Zhao, Zhigen & Wang, Jun & Hou, Fengzhen, 2017. "Network analysis in detection of early-stage mild cognitive impairment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 113-119.

    More about this item

    Keywords

    sequential analysis; change-point detection; compound decision; false non-discovery rate; large-scale inference;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ehl:lserod:111010. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.