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Activity Number: 448
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 2:45 PM
Sponsor: Biometrics Section
Abstract #321557
Title: Modeling Heterogeneity in Motor Learning Using Heteroskedastic Functional Principal Components
Author(s): Daniel Backenroth* and Jeff Goldsmith and Tomoko Kitago and John Krakauer
Companies: Columbia Mailman School of Public Health and Columbia Mailman School of Public Health and Columbia University Medical Center and Johns Hopkins School of Medicine
Keywords: Functional Principal Components Analysis ; Variational Bayes ; Mixed Models ; Smoothing

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.

Authors who are presenting talks have a * after their name.

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