Abstract: This article discusses methods of estimating the variation in product quality characteristics measured at several stages in a manufacturing process. By determining which stages contribute most to variation one can focus variation reduction activities more effectively. A multivariate normal Markov process is used to model the variation in characteristics. Methods that deal with measurement error and missing data are introduced through a state space formulation.
Key words and phrases: E-M algorithm, Kalman filter, measurement error, missing data.