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dc.contributor.authorMartin, Ryan
dc.contributor.authorTokdar, Surya T.
dc.date.accessioned2013-11-19T21:56:11Z
dc.date.available2013-11-19T21:56:11Z
dc.date.issued2011-11
dc.identifier.bibliographicCitationMartin R, Tokdar ST. A nonparametric empirical Bayes framework for large-scale multiple testing. Biostatistics. 2012 Jul;13(3):427-39. doi: 10.1093/biostatistics/kxr039.en_US
dc.identifier.issn1468-4357
dc.identifier.urihttp://hdl.handle.net/10027/10566
dc.descriptionThis is a pre-copy-editing, author-produced PDF of an article accepted for publication in Biostatistics following peer review. The definitive publisher-authenticated version Martin R, Tokdar ST. A nonparametric empirical Bayes framework for large-scale multiple testing. Biostatistics. 2012 Jul;13(3):427-39. doi: 10.1093/biostatistics/kxr039. is available online at: http://biostatistics.oxfordjournals.org/en_US
dc.description.abstractWe propose a exible and identi able version of the two-groups model, moti- vated by hierarchical Bayes considerations, that features an empirical null and a semiparametric mixture model for the non-null cases. We use a computationally e cient predictive recursion marginal likelihood procedure to estimate the model parameters, even the nonparametric mixing distribution. This leads to a nonpara- metric empirical Bayes testing procedure, which we call PRtest, based on thresh- olding the estimated local false discovery rates. Simulations and real-data examples demonstrate that, compared to existing approaches, PRtest's careful handling of the non-null density can give a much better t in the tails of the mixture distribution which, in turn, can lead to more realistic conclusions.en_US
dc.language.isoen_USen_US
dc.publisherOxford University Pressen_US
dc.subjectDirichlet processen_US
dc.subjectmarginal likelihooden_US
dc.subjectmixture modelen_US
dc.subjectpredictive recursionen_US
dc.subjecttwo-groups modelen_US
dc.titleA nonparametric empirical Bayes framework for large-scale multiple testingen_US
dc.typeArticleen_US


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