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Longitudinal high-dimensional principal components analysis with application to diffusion tensor imaging of multiple sclerosis

By Vadim Zipunnikov and others
We develop a flexible framework for modeling high-dimensional imaging data observed longitudinally. The approach decomposes the observed variability of repeatedly measured high-dimensional observations into three additive components: a subject-specific imaging random intercept that quantifies the cross-sectional variability, a subject-specific imaging slope that quantifies the dynamic irreversible deformation over multiple realizations,... Show more
January 19, 2015
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Longitudinal high-dimensional principal components analysis with application to diffusion tensor imaging of multiple sclerosis
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