Title

Applying state space to SPC: Monitoring multivariate time series

Document Type

Article

Date of Original Version

5-1-2004

Abstract

Monitoring cross-sectional and serially interdependent processes has become a new issue in statistical process control (SPC). In up-to-date SPC literature, Kalman filtering was reported to monitor univariate autocorrelated processes. This paper applies a Kalman filter or state-space method for SPC to monitoring multivariate time series. We use Aoki's approach to estimate the parameter matrices of a state-space model. Multivariate Hotelling T2 control charts are employed to monitor the residuals of the state-space. Examples of this approach are illustrated. © 2004 Taylor & Francis Ltd.

Publication Title, e.g., Journal

Journal of Applied Statistics

Volume

31

Issue

4

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