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

Lei Qiao and Bing Wang ()
Additional contact information
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

Mathematics, 2024, vol. 12, issue 10, 1-12

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.

Keywords: distribution-free; kernel-based; multivariate CUSUM multi-chart (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/10/1473/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/10/1473/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:10:p:1473-:d:1391339

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().