The Dynamic Window Approach as a Tool to Improve Performance of Nonparametric Self-Starting Control Charts

Claudio Giovanni Borroni (), Manuela Cazzaro and Paola Maddalena Chiodini
Additional contact information
Claudio Giovanni Borroni: Department of Statistics and Quantitative Methods, University of Milano-Bicocca, 20126 Milan, Italy
Manuela Cazzaro: Department of Statistics and Quantitative Methods, University of Milano-Bicocca, 20126 Milan, Italy
Paola Maddalena Chiodini: Department of Statistics and Quantitative Methods, University of Milano-Bicocca, 20126 Milan, Italy

Mathematics, 2025, vol. 13, issue 6, 1-25

Abstract: The change-point model is an established methodology for the construction of self-starting control charts. Change-point charts are often nonparametric in order to be independent from any specific assumptions about the process distribution. Nonetheless, this methodology is usually implemented by considering all possible splits of a given stream of observations into two adjacent sub-samples. This can make the recent observations too influential and the chart’s signals too dependent on limited evidence. This paper proposes to correct such a distortion by using a window approach, which forces the use of only comparisons based on sub-samples of the same size. The resulting charts are “omnibus”, with respect to their having any kind of shift and also any direction of such shifts. To prove this, this paper focuses on a chart based on the Cramér–von Mises test. We report a simulation study evaluating the average number of readings to obtain a signal after a known shift has occurred. We conclude that, beyond being stable with respect to the direction of the shift, the new chart overcomes its competitors when the distribution heads toward regularity. Finally, the new approach is shown to have successful application to a real problem about air quality.

Keywords: statistical process monitoring; self-starting control charts; nonparametric control charts; change-point model; window approach; Monte Carlo simulations; air quality data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/6/938/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/6/938/ (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:13:y:2025:i:6:p:938-:d:1610547

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 ().