Abstract Details
Activity Number:
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441
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #313194
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View Presentation
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Title:
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Varying-Smoother Models for Brain Development
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Author(s):
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Philip T. Reiss*+ and Lei Huang and Huaihou Chen and Stan Colcombe
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Companies:
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New York University School of Medicine and Johns Hopkins University and New York University and Nathan Kline Institute
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Keywords:
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curve estimation ;
functional principal component analysis ;
linear mixed effects model ;
longitudinal neuroimaging ;
magnetic resonance imaging ;
neurodevelopmental trajectory
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Abstract:
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Motivated by studies of human brain development, we consider estimation of a bivariate smooth function when the data are spatially smooth functional responses, but scientific interest centers on smoothness in the temporal direction. Analogously to varying-coefficient models, which are linear with respect to time, the "varying-smoother'' models that we consider exhibit nonlinear dependence on time that varies smoothly over space. We focus on two spline-based approaches to estimating varying-smoother models: (i) methods that apply a tensor product penalty, and (ii) two-step methods consisting of an initial smooth in the temporal direction, followed by a postprocessing step. We propose a novel approach to pointwise model selection, and discuss extensions to longitudinal neuroimaging studies.
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Authors who are presenting talks have a * after their name.
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