Abstract Details
Activity Number:
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477
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Type:
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Invited
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Date/Time:
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Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #310741
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Title:
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A Bayesian Multivariate Smoothing Spline Model for Spatial-Temporal Data
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Author(s):
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Xiaofeng Wang*+ and Ryan Yue
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Companies:
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Cleveland Clinic Lerner Research Institute and City University of New York
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Keywords:
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Bayesian inference ;
Gaussian Markov random field ;
MCMC ;
fMRI ;
spatial-temporal data ;
multivariate smoothing spline
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Abstract:
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We consider a novel Bayesian approach to model spatial-temporal data. It is based on the idea of multivariate smoothing spline with correlated error components and correlated derivatives of the curves. The smoothing spline prior accounts for temporal correlations while a 3D Gaussian Markov random field (GMRF) prior is proposed to take care of spatial correlations. We develop efficient Markov Chain Monte Carlo (MCMC) algorithms for Bayesian computation. The effectiveness of the method is demonstrated in numerical simulations. Finally, Our approach is applied to a study of functional connectivity in functional magnetic resonance imaging (fMRI).
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Authors who are presenting talks have a * after their name.
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