Application of Resampling Methods to the Choice of Dimension in Principal Component Analysis

  • Ph. Besse
  • A. de Falguerolles
Conference paper
Part of the Statistics and Computing book series (SCO)


This paper investigates the problem of the choice of dimension in Principal Component Analysis (PCA). PCA is introduced as a model; a loss function assessing the stability of the fit is considered. The choice of dimension then amounts to the minimisation of an expected loss which has to be estimated. This is achieved by resampling methods. Different bootstrap and jackknife estimates are presented. The behaviour of these estimates are investigated on artificial data and on real data. The resulting choices are confronted with those given by naïve rules.


Principal Component Analysis Optimal Dimension Bootstrap Jackknife Perturbation Theory. 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Ph. Besse
    • 1
  • A. de Falguerolles
    • 1
  1. 1.Laboratoire de Statistique et ProbabilitésU.A. CNRS D0745, Université Paul SabatierToulouse cedexFrance

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