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Functional principal components

  • Lajos Horváth
  • Piotr Kokoszka
Chapter
Part of the Springer Series in Statistics book series (SSS, volume 200)

Abstract

This chapter introduces one of the most fundamental concepts of FDA, that of the functional principal components (FPC’s). FPC’s allow us to reduce the dimension of infinitely dimensional functional data to a small finite dimension in an optimal way. In Sections 3.1 and 3.2, we introduce the FPC’s from two angles, as coordinates maximizing variability, and as an optimal orthonormal basis. In Section 3.3, we identify the FPC’s with the eigenfunctions of the covariance operator, and show how its eigenvalues decompose the variance of the functional data. We conclude with Section 3.4 which explains how to compute the FPC’s in the R package fda.

Keywords

Functional Data Covariance Operator Functional Data Analysis Schmidt Operator Functional Object 
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

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Lajos Horváth
    • 1
  • Piotr Kokoszka
    • 2
  1. 1.Department of MathematicsUniversity of UtahSalt Lake CityUSA
  2. 2.Department of StatisticsColorado State UniversityFort CollinsUSA

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