Multivariate Process Variability Monitoring Improvements, Ph.D. Dissertation Sharif University of Technology ; Akhavan Niaki, Taghi (Supervisor)
Abstract
We consider finding some efficient control schemes for multivariate variability monitoring with capability of working with individual observations. To do this, the existing efficient control charts for multivariate variability monitoring are studied first and it is determined that the and control statistics, defined by individual observations, estimate the covariance matrix quite well. However, the control method that is based on monitoring the trace of these matrices is not necessarily the best. Thus, by applying the first and the second norm on these two statistics, four new control schemes, namely MEWMSL1, MEWMVL1, MEWMVL1 and MEWMVL2are proposed. Performance comparison results show...
Cataloging briefMultivariate Process Variability Monitoring Improvements, Ph.D. Dissertation Sharif University of Technology ; Akhavan Niaki, Taghi (Supervisor)
Abstract
We consider finding some efficient control schemes for multivariate variability monitoring with capability of working with individual observations. To do this, the existing efficient control charts for multivariate variability monitoring are studied first and it is determined that the and control statistics, defined by individual observations, estimate the covariance matrix quite well. However, the control method that is based on monitoring the trace of these matrices is not necessarily the best. Thus, by applying the first and the second norm on these two statistics, four new control schemes, namely MEWMSL1, MEWMVL1, MEWMVL1 and MEWMVL2are proposed. Performance comparison results show...
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