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Activity Number:
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448
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2016 : 2:00 PM to 2:45 PM
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Sponsor:
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Biometrics Section
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Abstract #321557
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Title:
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Modeling Heterogeneity in Motor Learning Using Heteroskedastic Functional Principal Components
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Author(s):
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Daniel Backenroth* and Jeff Goldsmith and Tomoko Kitago and John Krakauer
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Companies:
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Columbia Mailman School of Public Health and Columbia Mailman School of Public Health and Columbia University Medical Center and Johns Hopkins School of Medicine
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Keywords:
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Functional Principal Components Analysis ;
Variational Bayes ;
Mixed Models ;
Smoothing
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Abstract:
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We propose a novel method for estimating population-level and subject-specific effects of covariates on the variability of functional data. We extend the functional principal components analysis framework by modeling the variance of principal component scores as a function of covariates and subject-specific random effects. In a setting where principal components are largely invariant across subjects and covariate values, modeling the variance of these scores provides a flexible and interpretable way to explore factors that affect the variability of functional data. Our work is motivated by a novel dataset from an experiment assessing upper extremity motor control, and quantifies the reduction in motion variance associated with skill learning.
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Authors who are presenting talks have a * after their name.
