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Spatial Bayesian Variable Selection with Application to Functional Magnetic Resonance Imaging (fMRI)

dc.contributor.advisor Li, Fan
dc.contributor.author Yang, Ying
dc.date.accessioned 2012-05-29T16:37:22Z
dc.date.available 2014-05-19T04:30:04Z
dc.date.issued 2011
dc.identifier.uri https://hdl.handle.net/10161/5620
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>
dc.subject Statistics
dc.subject Bayesian variable selection
dc.subject fMRI
dc.subject spatial
dc.title Spatial Bayesian Variable Selection with Application to Functional Magnetic Resonance Imaging (fMRI)
dc.type Master's thesis
dc.department Statistical Science
duke.embargo.months 24


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