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A spatial rank‐based multivariate EWMA control chart
Author(s) -
Zou Changliang,
Wang Zhaojun,
Tsung Fugee
Publication year - 2012
Publication title -
naval research logistics (nrl)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 68
eISSN - 1520-6750
pISSN - 0894-069X
DOI - 10.1002/nav.21475
Subject(s) - ewma chart , multivariate statistics , control chart , computer science , statistical process control , statistic , statistics , nonparametric statistics , data mining , rank (graph theory) , process (computing) , mathematics , machine learning , combinatorics , operating system
Abstract Nonparametric control charts are useful in statistical process control when there is a lack of or limited knowledge about the underlying process distribution, especially when the process measurement is multivariate. This article develops a new multivariate self‐starting methodology for monitoring location parameters. It is based on adapting the multivariate spatial rank to on‐line sequential monitoring. The weighted version of the rank‐based test is used to formulate the charting statistic by incorporating the exponentially weighted moving average control scheme. It is robust to non‐normally distributed data, easy to construct, fast to compute and also very efficient in detecting multivariate process shifts, especially small or moderate shifts which occur when the process distribution is heavy‐tailed or skewed. As it avoids the need for a lengthy data‐gathering step before charting and it does not require knowledge of the underlying distribution, the proposed control chart is particularly useful in start‐up or short‐run situations. A real‐data example from white wine production processes shows that it performs quite well. © 2012 Wiley Periodicals, Inc. Naval Research Logistics 59: 91–110, 2012