Dimension constraints improve hypothesis testing for large-scale, graph-associated, brain-image data.
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ABSTRACT:
SUBMITTER: Vo T
PROVIDER: S-EPMC9295049 | biostudies-literature | 2022 Jul
REPOSITORIES: biostudies-literature
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Dimension constraints improve hypothesis testing for large-scale, graph-associated, brain-image data.
Vo Tien T Mishra Akshay A Ithapu Vamsi V Singh Vikas V Newton Michael A MA
Biostatistics (Oxford, England) 20220701 3
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 in settings where non-null cases form connected subgraphs, and it does so by regularizing parameter contrasts between testing units. Simulations show that GraphMM controls the FDR in a variety of settings, though it may lose control with exc ...[more]
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