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Some alternatives for conditional principal component analysis

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  • Ramon Nonell
  • Santiago Thió‐Henestrosa
  • Tomàs Aluja‐Banet

Abstract

Classically principal component analysis is one of the most used techniques for exploring the multivariate association pattern of variables. On the other hand, conditioning is one of the most promising ideas for controlling the variability of observed data. Here we present a review of some conditioning methods from the analysis of residuals of a parametric model to the analysis of the local variation defined by means of a non‐oriented graph of individuals, this variation being defined from the deviation from a local mean or alternatively from the differences among contiguous vertices. We will compare these approaches and will show that under some conditions they give comparable results. Finally, we will present an example of application to illustrate the results previously stated. Copyright © 2000 John Wiley & Sons, Ltd.

Suggested Citation

  • Ramon Nonell & Santiago Thió‐Henestrosa & Tomàs Aluja‐Banet, 2000. "Some alternatives for conditional principal component analysis," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 16(2), pages 147-158, April.
    • Handle: RePEc:wly:apsmbi:v:16:y:2000:i:2:p:147-158
    DOI: 10.1002/1526-4025(200004/06)16:23.0.CO;2-7
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