Applying State Space to SPC: Monitoring Multivariate Time Series

Xia Pan and Jeffrey Jarrett ()

Journal of Applied Statistics, 2004, vol. 31, issue 4, 397-418

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.

Keywords: Quality Control Charts; Spc; State-space; Multivariate Time Series; Aoki's Approach (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (10)

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DOI: 10.1080/02664760410001681701

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