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A new multivariate variability control chart based on a covariance matrix combination
Author(s) -
Alfaro Jose Luis,
Ortega Juan Fco.
Publication year - 2018
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2412
Subject(s) - control chart , multivariate statistics , chart , statistics , covariance matrix , covariance , variance (accounting) , multivariate analysis of variance , computer science , statistical process control , shewhart individuals control chart , multivariate normal distribution , ewma chart , mathematics , process (computing) , operating system , accounting , business
In the field of multivariate quality control, there are many control charts related to the process mean but few options addressing process variability. Variability control charts have two main drawbacks: the first relates to the number of parameters to tune and the second relates to how changes in the mean affect the performance of these charts. Thus, in this paper, we propose a new multivariate variability control chart, called the multivariate exponentially weighted covariance matrix combination, which solves these two problems. The results show that this new chart performs well in the detection of changes in variance when the mean does not change and outperforms other charts when the mean does change.