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Longitudinal Functional Principal Component Analysis

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Recent Advances in Functional Data Analysis and Related Topics

Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

Abstract

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.

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References

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Author information

Authors and Affiliations

  1. Ludwig-Maximilians-Universität München, Munich, Germany

    Sonja Greven

  2. Johns Hopkins University, Baltimore, USA

    Ciprian Crainiceanu & Brian Caffo

  3. National Institutes of Health, Bethesda, USA

    Daniel Reich

Authors
  1. Sonja Greven
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  2. Ciprian Crainiceanu
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  3. Brian Caffo
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  4. Daniel Reich
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Corresponding author

Correspondence to Sonja Greven .

Editor information

Editors and Affiliations

  1. Toulouse University, Mathematics Toulouse Institute, Narbonne road 118, Toulouse, 31062, France

    Frédéric Ferraty

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© 2011 Springer-Verlag Berlin Heidelberg

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Greven, S., Crainiceanu, C., Caffo, B., Reich, D. (2011). Longitudinal Functional Principal Component Analysis. In: Ferraty, F. (eds) Recent Advances in Functional Data Analysis and Related Topics. Contributions to Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2736-1_23

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