Abstract
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 (Brillinger, 1981) CitationRef CitationID Omitted tag 1 bachieve is applied to incorporate dependencies within time series data. We use the information obtained from graphical models to improve pattern detection based on PCA.
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Fried, R., Gather, U., Imhoff, M., Keller, M., Lanius, V. (2002). Combining Graphical Models and PCA for Statistical Process Control. In: Härdle, W., Rönz, B. (eds) Compstat. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57489-4_32
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DOI: https://doi.org/10.1007/978-3-642-57489-4_32
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1517-7
Online ISBN: 978-3-642-57489-4
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