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Functional Principal Components Analysis with Survey Data

  • Hervé Cardot
  • Mohamed Chaouch
  • Camelia Goga
  • Catherine Labruère
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

This work aims at performing Functional Principal Components Analysis (FPCA) thanks to Horvitz-Thompson estimators when the curves are collected with survey sampling techniques. Linearization approaches based on the infiuence function allow us to derive estimators of the asymptotic variance of the eigenelements of the FPCA. The method is illustrated with simulations which confirm the good properties of the linearization technique.

Keywords

Covariance Operator Asymptotic Variance Linearization Technique Functional Data Analysis Functional Principal Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Physica-Verlag Heidelberg 2008

Authors and Affiliations

  • Hervé Cardot
    • 1
  • Mohamed Chaouch
    • 1
  • Camelia Goga
    • 1
  • Catherine Labruère
    • 1
  1. 1.Institut de Mathématiques de BourgogneUniversité de BourgogneFrance

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