Longitudinal Functional Principal Component Analysis

  • Sonja Greven
  • Ciprian Crainiceanu
  • Brian Caffo
  • Daniel Reich
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)


We introduce models for the analysis of functional data observed at multiple time points. The model can be viewed as the functional analog of the classical mixed effects model where random effects are replaced by random processes. Computational feasibility is assured by using principal component bases. The methodology is motivated by and applied to a diffusion tensor imaging (DTI) study on multiple sclerosis.


Principal Component Analysis Multiple Sclerosis Random Effect Pattern Recognition Stochastic Process 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sonja Greven
    • 1
  • Ciprian Crainiceanu
    • 2
  • Brian Caffo
    • 2
  • Daniel Reich
    • 3
  1. 1.Ludwig-Maximilians-Universität MünchenMunichGermany
  2. 2.Johns Hopkins UniversityBaltimoreUSA
  3. 3.National Institutes of HealthBethesdaUSA

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