EconPapers    
Economics at your fingertips  
 

A non-parametric CUSUM control chart for process distribution change detection and change type diagnosis

Shiwang Hou and Keming Yu

International Journal of Production Research, 2021, vol. 59, issue 4, 1166-1186

Abstract: Non-parametric control charts have been good alternatives to parametric control charts when little information is known about the type of process distribution or the value of parameters. Most approaches proposed by the current literature monitor either location or scale change in batch mode and their performance is discounted when monitoring distribution change in both location and scale simultaneously in a sequential pattern. This paper proposed a log-likelihood-ratio-based non-parametric cumulative sum (CUSUM) control chart to monitor arbitrary distribution change and diagnose the detailed change type simultaneously. By integrating the superiority of log-likelihood ratio test to detect any change of distribution and CUSUM chart to detect a small change, the proposed approach can detect small potential changes in location, scale and shape; and provide detailed information about change type when control chart gives a signal. Comparison results with many other non-parametric approaches were provided by numerical simulation and the results of an application case demonstrate the effectiveness of the proposed approach.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2020.1721588 (text/html)
Access to full text is restricted to subscribers.

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:taf:tprsxx:v:59:y:2021:i:4:p:1166-1186

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2020.1721588

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-04-12
Handle: RePEc:taf:tprsxx:v:59:y:2021:i:4:p:1166-1186