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A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk

To correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new methodologies of selective inference could potentially improve power while retaining statistical guarantees, especially those that enable exploration of test statistics using aux...

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Detalles Bibliográficos
Autores principales: Yurko, Ronald, G’Sell, Max, Roeder, Kathryn, Devlin, Bernie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334489/
https://www.ncbi.nlm.nih.gov/pubmed/32522875
http://dx.doi.org/10.1073/pnas.1918862117