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Optimal multiple testing under a Gaussian prior on the effect sizes
We develop a new method for large-scale frequentist multiple testing with Bayesian prior information. We find optimal [Formula: see text]-value weights that maximize the average power of the weighted Bonferroni method. Due to the nonconvexity of the optimization problem, previous methods that accoun...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4813057/ https://www.ncbi.nlm.nih.gov/pubmed/27046938 http://dx.doi.org/10.1093/biomet/asv050 |
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author | Dobriban, Edgar Fortney, Kristen Kim, Stuart K. Owen, Art B. |
author_facet | Dobriban, Edgar Fortney, Kristen Kim, Stuart K. Owen, Art B. |
author_sort | Dobriban, Edgar |
collection | PubMed |
description | We develop a new method for large-scale frequentist multiple testing with Bayesian prior information. We find optimal [Formula: see text]-value weights that maximize the average power of the weighted Bonferroni method. Due to the nonconvexity of the optimization problem, previous methods that account for uncertain prior information are suitable for only a small number of tests. For a Gaussian prior on the effect sizes, we give an efficient algorithm that is guaranteed to find the optimal weights nearly exactly. Our method can discover new loci in genome-wide association studies and compares favourably to competitors. An open-source implementation is available. |
format | Online Article Text |
id | pubmed-4813057 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-48130572016-12-01 Optimal multiple testing under a Gaussian prior on the effect sizes Dobriban, Edgar Fortney, Kristen Kim, Stuart K. Owen, Art B. Biometrika Articles We develop a new method for large-scale frequentist multiple testing with Bayesian prior information. We find optimal [Formula: see text]-value weights that maximize the average power of the weighted Bonferroni method. Due to the nonconvexity of the optimization problem, previous methods that account for uncertain prior information are suitable for only a small number of tests. For a Gaussian prior on the effect sizes, we give an efficient algorithm that is guaranteed to find the optimal weights nearly exactly. Our method can discover new loci in genome-wide association studies and compares favourably to competitors. An open-source implementation is available. Oxford University Press 2015-12 2015-11-04 /pmc/articles/PMC4813057/ /pubmed/27046938 http://dx.doi.org/10.1093/biomet/asv050 Text en © 2015 Biometrika Trust |
spellingShingle | Articles Dobriban, Edgar Fortney, Kristen Kim, Stuart K. Owen, Art B. Optimal multiple testing under a Gaussian prior on the effect sizes |
title | Optimal multiple testing under a Gaussian prior on the effect sizes |
title_full | Optimal multiple testing under a Gaussian prior on the effect sizes |
title_fullStr | Optimal multiple testing under a Gaussian prior on the effect sizes |
title_full_unstemmed | Optimal multiple testing under a Gaussian prior on the effect sizes |
title_short | Optimal multiple testing under a Gaussian prior on the effect sizes |
title_sort | optimal multiple testing under a gaussian prior on the effect sizes |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4813057/ https://www.ncbi.nlm.nih.gov/pubmed/27046938 http://dx.doi.org/10.1093/biomet/asv050 |
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