Combining graphical models and PCA for statistical process control. (English) Zbl 1439.62019
Härdle, Wolfgang (ed.) et al., COMPSTAT. Proceedings in computational statistics. 15th symposium, Berlin, Germany, August 24–28, 2002. Heidelberg: Physica-Verlag. 237-242 (2002).
Summary: Principal component analysis (PCA) is frequently used for detection of common structures in multivariate data, e.g. in statistical process control. Critical issues are the choice of the number of principal components and their interpretation. These tasks become even more difficult when dynamic PCA [D. R. Brillinger, Time series. Data analysis and theory. Expand. ed. San Francisco etc.: Holden-Day, Inc. (1981; Zbl 0486.62095)] is applied to incorporate dependencies within time series data. We use the information obtained from graphical models to improve pattern detection based on PCA.
For the entire collection see [Zbl 1023.00020].
For the entire collection see [Zbl 1023.00020].
MSC:
| 62-08 | Computational methods for problems pertaining to statistics |
| 62P30 | Applications of statistics in engineering and industry; control charts |
| 62H25 | Factor analysis and principal components; correspondence analysis |
| 62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |
Citations:
Zbl 0486.62095References:
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