Distribution-free Phase-I scheme for location, scale and skewness shifts with an application in monitoring customers' waiting time

Akira Suzuki, Hidetoshi Murakami and Amitava Mukherjee

Journal of Applied Statistics, 2023, vol. 50, issue 4, 827-847

Abstract: Phase-I analysis of historical data from a statistical process is a strategic problem in Statistical Process Monitoring and control. Before the establishment of process stability, it is challenging to model historical data. Consequently, a distribution-free approach is a natural choice in Phase-I monitoring. Existing distribution-free Phase-I control charts are suitable for detecting instability in location and scale parameters only and are often insensitive in complex processes involving skewness or shape parameters. A new Phase-I control chart is proposed to identify more general shifts, including location, scale and skewness. The proposed Phase-I scheme is efficient in such a situation. The proposed Phase-I scheme uses subsamples, and the plotting statistic is based on the omnibus multi-sample linear rank statistic corresponding to the location, scale and skewness shifts. The new scheme can identify subsamples that are not in control, and it can also indicate one or more process parameters where a deviation has occurred. The encouraging performance of the proposed scheme is established with a large-scale numerical study based on Monte-Carlo in detecting shifts of various nature in a comprehensive class of situations. An illustration based on monitoring the waiting time data from a customer service centre is given. Some concluding remarks and some future research problems are also offered.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2021.1994530 (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:japsta:v:50:y:2023:i:4:p:827-847

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2021.1994530

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

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