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Dimension constraints improve hypothesis testing for large-scale, graph-associated, brain-image data
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 con...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9295049/ https://www.ncbi.nlm.nih.gov/pubmed/33616173 http://dx.doi.org/10.1093/biostatistics/kxab001 |