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dc.contributor.advisor Li, Fan en_US
dc.contributor.author Yang, Ying en_US
dc.date.accessioned 2012-05-29T16:37:22Z
dc.date.issued 2011 en_US
dc.identifier.uri http://hdl.handle.net/10161/5620
dc.description Thesis en_US
dc.description.abstract <p>Functional magnetic resonance imaging (fMRI) is a major neuroimaging methodology and have greatly facilitate basic cognitive neuroscience research. However, there are multiple statistical challenges in the analysis of fMRI data, including, dimension reduction, multiple testing and inter-dependence of the MRI responses. In this thesis, a spatial Bayesian variable selection (BVS) model is proposed for the analysis of multi-subject fMRI data. The BVS framework simultaneously account for uncertainty in model specific parameters as well as the model selection process, solving the multiple testing problem. A spatial prior incorporate the spatial relationship of the MRI response, accounting for their inter-dependence. Compared to the non-spatial BVS model, the spatial BVS model enhances the sensitivity and accuracy of identifying activated voxels.</p> en_US
dc.subject Statistics en_US
dc.subject Bayesian variable selection en_US
dc.subject fMRI en_US
dc.subject spatial en_US
dc.title Spatial Bayesian Variable Selection with Application to Functional Magnetic Resonance Imaging (fMRI) en_US
dc.type Thesis en_US
dc.department Statistical Science en_US
duke.embargo.months 24 en_US
duke.embargo.release 2014-05-19

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