Abstract Details
Activity Number:
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422
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Type:
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Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #310223 |
Title:
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Functional Principal Components Mixture Regression with Application to CT Image Data
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Author(s):
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Lucy Robinson*+ and Sriram Balasubramanian and Silpa Reddy
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Companies:
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Drexel University and Drexel University and Drexel University
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Keywords:
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image data ;
functional data analysis ;
mixture models
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Abstract:
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We propose a novel functional principal components mixture regression model with application to CT image data. As a motivating example, we consider ribcage images of pediatric subjects with thoracic deformities. Image data are used as covariates in a regression model predicting scalar pulmonary function measures. Each rib pair can be considered as a functional data object, and within each subject we may have a mixture of ribs of a normative shape and ribs exhibiting some deformity. Variation across rib pairs within subjects and variation between subjects are described using a multilevel functional principal components analysis. The relationship between the scalar response and the functional principal components is described by a mixture regression model in which the regression function depends on an unobserved latent class variable. Model parameters are estimated in a Bayesian framework using MCMC.
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Authors who are presenting talks have a * after their name.
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