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Activity Number: 328
Type: Invited
Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract #314364
Title: Modeling Covariance in Functional Data Analysis
Author(s): Giles Hooker* and Cecelia Earls
Companies: Cornell University and Cornell University
Keywords: Functional Data Analysis ; Covariance ; Registration ; Factor Analysis ; Smoothing ; variational Bayes
Abstract:

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.


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

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