2019/12/20 by Jimoh Olawale Ajadi, Ajadi, Jimoh Olawale, Zezhong Wang +3
Decision Sciences · Mathematics · #Advanced Statistical Methods and Models #Advanced Statistical Process Monitoring #CUSUM #Computer science #Control chart #Covariance #Covariance matrix #Data mining #EWMA chart #FOS: Computer and information sciences #Mathematics #Methodology (stat.ME) #Multivariate analysis #Multivariate statistics #Parametric statistics #Process (computing) #Shewhart individuals control chart #Statistical process control #Statistics #stat.ME
paper · pdf · doi:10.48550/arxiv.1912.09755
published in arXiv (Cornell University) (Cornell University) · 43 pages, 2 Figures, 9 Tables
arxiv created 2019/12/20 · openalex publication_date 2019/12/20 · arxiv updated 2019/12/23 · openalex created_date 2022/07/26 · openalex updated_date 2026/08/06
A multivariate control chart is designed to monitor process parameters of\nmultiple correlated quality characteristics. Often data on multivariate\nprocesses are collected as individual observations, i.e. as vectors one at the\ntime. Various control charts have been proposed in the literature to monitor\nthe covariance matrix of a process when individual observations are collected.\nIn this study, we review this literature; we find 30 relevant articles from the\nperiod 1987-2019. We group the articles into five categories. We observe that\nless research has been done on CUSUM, high-dimensional and non-parametric type\ncontrol charts for monitoring the process covariance matrix. We describe each\nproposed method, state their advantages, and limitations. Finally, we give\nsuggestions for future research.\n