Applying state space to SPC: Monitoring multivariate time series
Date of Original Version
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
Pan, Xia, and Jeffrey Jarrett. "Applying state space to SPC: Monitoring multivariate time series." Journal of Applied Statistics 31, 4 (2004): 397-418. doi: 10.1080/02664760410001681701.