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• W2155049373 abstract "Identifying leading measurement units from a large collection is a common inference task in various domains of large-scale inference. Testing approaches, which measure evidence against a null hypothesis rather than effect magnitude, tend to overpopulate lists of leading units with those associated with low measurement error. By contrast, local maximum likelihood (ML) approaches tend to favor units with high measurement error. Available Bayesian and empirical Bayesian approaches rely on specialized loss functions that result in similar deficiencies. We describe and evaluate a generic empirical Bayesian ranking procedure that populates the list of top units in a way that maximizes the expected overlap between the true and reported top lists for all list sizes. The procedure relates unit-specific posterior upper tail probabilities with their empirical distribution to yield a ranking variable. It discounts high-variance units less than popular non-ML methods and thus achieves improved operating characteristics in the models considered." @default.
• W2155049373 created "2016-06-24" @default.
• W2155049373 creator A5003087080 @default.
• W2155049373 creator A5072759658 @default.
• W2155049373 date "2015-11-08" @default.
• W2155049373 modified "2023-10-16" @default.
• W2155049373 title "Making the Cut: Improved Ranking and Selection for Large-Scale Inference" @default.
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• W2155049373 doi "https://doi.org/10.1111/rssb.12131" @default.
• W2155049373 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4996506" @default.
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• W2155049373 hasPublicationYear "2015" @default.
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