Dimension constraints improve hypothesis testing for large-scale, graph-associated, brain-image data
{{output}}
For large-scale testing with graph-associated data, we present an empirical Bayes mixture technique to score local false-discovery rates (FDRs). Compared to procedures that ignore the graph, the proposed Graph-based Mixture Model (GraphMM) method gains power i... ...
