Abstract Details
Activity Number:
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328
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Type:
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Invited
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Date/Time:
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Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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Abstract #314364
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Title:
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Modeling Covariance in Functional Data Analysis
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Author(s):
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Giles Hooker* and Cecelia Earls
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Companies:
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Cornell University and Cornell University
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Keywords:
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Functional Data Analysis ;
Covariance ;
Registration ;
Factor Analysis ;
Smoothing ;
variational Bayes
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Abstract:
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This talk presents Bayesian methods to model functional covariances. We demonstrate that smoothing a covariance can be achieved through the hyperparameters of an inverse Wishart process. Moreover estimates (and uncertainty) can be readily estimated through a variational Bayesian procedure. This provides access to model structures that incorporate latent functional variables and we demonstrate their use in the development of a combined registration and factor analysis model. Extensions into models incorporating covariates and spatial correlation between functions will be discussed.
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Authors who are presenting talks have a * after their name.
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