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Activity Number: 477
Type: Invited
Date/Time: Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #310741
Title: A Bayesian Multivariate Smoothing Spline Model for Spatial-Temporal Data
Author(s): Xiaofeng Wang*+ and Ryan Yue
Companies: Cleveland Clinic Lerner Research Institute and City University of New York
Keywords: Bayesian inference ; Gaussian Markov random field ; MCMC ; fMRI ; spatial-temporal data ; multivariate smoothing spline
Abstract:

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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