1. Multivariate exponentially weighted moving sample covariance control chart for monitoring covariance matrix
- Author
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Mona Ayoubi, Seyed A. Vaghefi, E. Hassan Nayebi, and Amirhossein Amiri
- Subjects
021103 operations research ,Covariance function ,Covariance matrix ,0211 other engineering and technologies ,02 engineering and technology ,Covariance ,Estimation of covariance matrices ,Matérn covariance function ,Scatter matrix ,Statistics ,0202 electrical engineering, electronic engineering, information engineering ,Law of total covariance ,Statistics::Methodology ,Rational quadratic covariance function ,020201 artificial intelligence & image processing ,Safety, Risk, Reliability and Quality ,Mathematics - Abstract
In this paper, a control chart is proposed to detect changes in the covariance matrix of a multivariate normal process, when sample size is one. The proposed chart statistic is constructed based on the exponentially weighted form of sample covariance matrix given by individual observation over time. Distance between the values of variance and covariance components in this multivariate exponentially weighted moving sample covariance matrix and, the in-control corresponding elements of process variance-covariance matrix provides a basis for process variability monitoring. The statistical performance of the proposed method is evaluated through the use of a Monte Carlo simulation. The results show the superiority of the proposed control chart performance especially in the case of incremental changes in covariance matrix.
- Published
- 2016
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