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.department | Statistical Science | |
| dc.description.abstract | 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. | |
| dc.identifier.uri | ||
| 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 | |
| duke.embargo.months | 24 |