IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i10p1473-d1391339.html
   My bibliography  Save this article

Kernel-Based Multivariate Nonparametric CUSUM Multi-Chart for Detection of Abrupt Changes

Author

Listed:
  • Lei Qiao

    (School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, China)

  • Bing Wang

    (School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, China)

Abstract

In many cases, it is difficult to obtain precise distributional information on multivariate sequences. Therefore, there is a need to propose nonparametric methods for monitoring multivariate sequences. This article discusses the multivariate change detection problem and utilizes the kernel function as the statistic to construct the nonparametric Multivariate Cumulative Sum multi-chart, under the assumption that there is prior information about the abrupt changes. Through theoretical and numerical analysis, we show that the proposed control chart is more effective compared to other existing control charts. The good monitoring effect of this method demonstrates a strong potential for application.

Suggested Citation

  • Lei Qiao & Bing Wang, 2024. "Kernel-Based Multivariate Nonparametric CUSUM Multi-Chart for Detection of Abrupt Changes," Mathematics, MDPI, vol. 12(10), pages 1-12, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1473-:d:1391339
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/10/1473/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/10/1473/
    Download Restriction: no
    ---><---

    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:gam:jmathe:v:12:y:2024:i:10:p:1473-:d:1391339. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.