Novel nonparametric control charts for monitoring dispersion of count data

Author:

Wang Zhiqiong1ORCID,Wu Qingxia1,Qiu Peihua2ORCID

Affiliation:

1. School of Management Tianjin University of Technology Tianjin China

2. Department of Biostatistics University of Florida Gainesville Florida USA

Abstract

AbstractCount data are common in practice. Statistical process control for count data thus has attracted much attention in recent years. Most existing methods on this topic focus on the detection of mean shifts of count data based on parametric modeling. However, their assumed parametric models (e.g., the Poisson probability model) are often invalid in practice due mainly to the potential impact of some latent confounding risk factors, which would lead to unreliable performance of the related control charts. In addition, it is highly desirable and important to monitor the dispersion of count data when the Poisson probability model is invalid. To this end, new nonparametric cumulative sum control charts and their corresponding self‐starting versions are suggested in this paper for monitoring the dispersion of count data based on data categorization and categorical data analysis. Numerical results show that the proposed method can provide more effective and robust monitoring of count data in comparison with some representative existing methods. A real‐data example is used to demonstrate its implementation and application.

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Management Science and Operations Research,Safety, Risk, Reliability and Quality

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Distribution-Free Control Charts Based on Multiple Runs: Advances and Applications in Supply Chain Management;International Journal of Mathematical, Engineering and Management Sciences;2024-08-01

2. Wilcoxon-Type Control Charts Based on Multiple Scans;Stats;2024-03-07

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