Change-Point detection for autocorrelated multivariate Poisson processes. Issue 3 (4th May 2023)

• Record Type: Journal Article
• Authors: Wang, Zhiqiong; He, Zhen; Shang, Yanfen; Ma, Yanhui
• Is Part Of: Quality technology & quantitative management. Volume 20:Issue 3(2023)
• Page Start: 384
• Page End: 404
• Publication Date: 2023-05-04
• Subjects: Autocorrelation -- change-point model -- generalized likelihood ratio test -- multivariate Poisson process -- statistical process control; Quality control -- Periodicals; Quality control -- Statistical methods -- Periodicals; Industrial management -- Periodicals; Industrial management; Management -- Research -- Methodology -- Periodicals; Qualitative research -- Periodicals; Management; Quality control; Quality control -- Statistical methods; Periodicals; 658.00721
• DOI: 10.1080/16843703.2022.2116903
• Languages: English
• ISSNs: 1684-3703

Abstract

ABSTRACT: Statistical process control for count data has attracted increasing attention in recent years. The need for efficient control charts suitable for autocorrelated multivariate count processes is well recognized. However, there is a scarcity of research aiming to take into account the autocorrelation among the multivariate count data. We are motivated to study the Phase I analysis of autocorrelated multivariate Poisson processes to detect and estimate change points in reference datasets. A change-point method is proposed based on the multivariate Poisson INAR(1) model by integrating generalized likelihood ratio tests with the binary segmentation procedure. A diagnostic procedure for pinpointing the location of the change point is also discussed. Our simulation results show that the proposed method has a better performance than the benchmark method, across a range of possible shifts, in the detection effectiveness and diagnostic accuracy. Furthermore, a real example from the manufacturing industry is used to illustrate the implementation steps of the proposed method.