Bayesian Multiresolution Variable Selection for Ultra-High Dimensional Neuroimaging Data.

ABSTRACT: Ultra-high dimensional variable selection has become increasingly important in analysis of neuroimaging data. For example, in the Autism Brain Imaging Data Exchange (ABIDE) study, neuroscientists are interested in identifying important biomarkers for early detection of the autism spectrum disorder (ASD) using high resolution brain images that include hundreds of thousands voxels. However, most existing methods are not feasible for solving this problem due to their extensive computational costs. In this work, we propose a novel multiresolution variable selection procedure under a Bayesian probit regression framework. It recursively uses posterior samples for coarser-scale variable selection to guide the posterior inference on finer-scale variable selection, leading to very efficient Markov

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC5885321 | biostudies-literature | 2018 Mar-Apr

REPOSITORIES: biostudies-literature