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Directionally sensitive MCUSUM mean charts
Author(s) -
Haq Abdul,
Sohrab Komal
Publication year - 2021
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2851
Subject(s) - control chart , multivariate statistics , context (archaeology) , statistical process control , computer science , statistics , process (computing) , multivariate normal distribution , chart , mathematics , artificial intelligence , geography , archaeology , operating system
Abstract In the context of a disease outbreak detection, a prime interest is to only detect increases in the process mean. It is thus desirable to have a directionally sensitive multivariate chart that can effectively detect either increases or decreases in the process mean vector. In this paper, with a suitable transformation that truncates multivariate observations either above or below the process mean vector, we propose one‐sided and two one‐sided MCUSUM charts for monitoring the mean of a multivariate normal process. Among the proposed charts, the one‐sided MCUSUM charts are directionally sensitive, while the two one‐sided MCUSUM charts are directionally insensitive. In addition, the fast initial response feature is also incorporated into the proposed charts to enhance their sensitivities against initial process shifts. The run length characteristics of these control charts are computed with the Monte Carlo simulation. Based on the run length comparisons, it is found that the proposed charts are more sensitive than the existing charts when detecting moderate‐to‐large shifts in the process mean. The proposed charts are also applied on real datasets to support the theory.