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Unpublished Paper
FUNCTIONAL PRINCIPAL COMPONENTS MODEL FOR HIGH-DIMENSIONAL BRAIN IMAGING
Johns Hopkins University, Dept. of Biostatistics Working Papers
  • Vadim Zipunnikov, Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
  • Brian S Caffo, Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
  • David M. Yousem, Johns Hopkins Hospital, Department of Radiology
  • Christos Davatzikos, University of Pennsylvania, Department of Radiology
  • Brian S. Schwartz, Johns Hopkins Bloomberg School of Public Health, Departments of Environmental Health Sciences and Epidemiology and Medicine
  • Ciprian Crainiceanu, Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
Date of this Version
1-18-2011
Abstract

We establish a fundamental equivalence between singular value decomposition (SVD) and functional principal components analysis (FPCA) models. The constructive relationship allows to deploy the numerical efficiency of SVD to fully estimate the components of FPCA, even for extremely high-dimensional functional objects, such as brain images. As an example, a functional mixed effect model is fitted to high-resolution morphometric (RAVENS) images. The main directions of morphometric variation in brain volumes are identified and discussed.

Citation Information
Vadim Zipunnikov, Brian S Caffo, David M. Yousem, Christos Davatzikos, et al.. "FUNCTIONAL PRINCIPAL COMPONENTS MODEL FOR HIGH-DIMENSIONAL BRAIN IMAGING" (2011)
Available at: http://works.bepress.com/ciprian_crainiceanu/17/