Dimension constraints improve hypothesis testing for large-scale, graph-associated, brain-image data
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For large-scale testing with graph-associated data, we present an empirical Bayes mixture technique to score local false discovery rates. Compared to empirical Bayes procedures that ignore the graph, the proposed method gains power in settings where non-null cases form connected subgraphs, and it does so by regularizing parameter contrasts between testing units. Simulations also show that GraphMM controls the false discovery rate in a variety of settings. We apply GraphMM to magnetic resonance imaging data from a study of brain changes associated with the onset of Alzheimer's disease.empirical Bayes, graph-respecting partition, GraphMM, image analysis, local false discovery rate, mixture model.
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