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...

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Detalles Bibliográficos
Autores principales: Vo, Tien, Mishra, Akshay, Ithapu, Vamsi, Singh, Vikas, Newton, Michael A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Articles
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

Internet

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

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