论文信息 - The analysis of process variation transmission with multivariate measurements

The analysis of process variation transmission with multivariate measurements

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 re- duction 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.

Daniel Y. T. Fong | J. F. Lawless

[1] John A. Nelder,et al. A Simplex Method for Function Minimization , 1965, Comput. J..

[2] William H. Press,et al. Numerical recipes , 1990 .

[3] J. Angus. Forecasting, Structural Time Series and the Kalman Filter , 1992 .

[4] D. Rubin. INFERENCE AND MISSING DATA , 1975 .

[5] M. A. Wincek. Applied Statistical Time Series Analysis , 1990 .

[6] N. L. Johnson,et al. Multivariate Analysis , 1958, Nature.

[7] J. A. Robinson,et al. Analysis of Variation Transmission in Manufacturing Processes—Part I , 1999 .

[8] D. Rubin. INFERENCE AND MISSING DATA , 1975 .

[9] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[10] Andrew Harvey,et al. Missing Observations in Dynamic Econometric Models: A Partial Synthesis , 1984 .

[11] N. Shephard,et al. Exact score for time series models in state space form , 1992 .

[12] Robert Tibshirani,et al. An Introduction to the Bootstrap , 1994 .

[13] Wayne A. Fuller,et al. Measurement Error Models , 1988 .

[14] Roderick J. A. Little,et al. Statistical Analysis with Missing Data , 1988 .