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Compstat pp 237-242 | Cite as

Combining Graphical Models and PCA for Statistical Process Control

  • Roland Fried
  • Ursula Gather
  • Michael Imhoff
  • Melanie Keller
  • Vivian Lanius

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.

Keywords

Time series analysis dimension reduction online monitoring pattern detection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Roland Fried
    • 1
  • Ursula Gather
    • 1
  • Michael Imhoff
    • 2
  • Melanie Keller
    • 1
  • Vivian Lanius
    • 1
  1. 1.Department of StatisticsUniversity of DortmundDortmundGermany
  2. 2.Surgical DepartmentCommunity Hospital DortmundDortmundGermany

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