What is it about?
The signal issued by a control chart triggers the process professionals to investigate the special cause. Change point methods simplify the efforts to search for and identify the special cause. In this study, using maximum likelihood estimation, a multivariate joint change point estimation procedure for monitoring both location and dispersion simultaneously is proposed. After a signal is generated by the simultaneously used Hotelling’s T^2 and/or generalized variance control charts, the procedure starts detecting the time of the change. The performance of the proposed method for several structural changes for the mean vector and covariance matrix is discussed.
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Why is it important?
In this study, a joint estimation of a change point is applied to multivariate normal processes for monitoring both mean and covariance shifts. An add-on change point estimation procedure is proposed for Phase II applications following the work of Dogu and DeveciKocakoc (2011), Nedumaran et al. (2000), Pignatiello and Samuel (2001), and Samuel et al. (1998a, 1998b). The proposed change point estimator is a complementary procedure for multivariate simultaneous monitoring tools. Our proposed estimator focuses on estimating the most likely location of the change after a single or combination multivariate control chart issues a signal.
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This page is a summary of: A Multivariate Change Point Detection Procedure for Monitoring Mean and Covariance Simultaneously, Communications in Statistics - Simulation and Computation, July 2013, Taylor & Francis, DOI: 10.1080/03610918.2012.661907.
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