{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T01:30:27Z","timestamp":1774056627152,"version":"3.50.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T00:00:00Z","timestamp":1676592000000},"content-version":"vor","delay-in-days":2658,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"US National Institutes of Health","doi-asserted-by":"publisher","award":["R21 HG006568"],"award-info":[{"award-number":["R21 HG006568"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"US National Institutes of Health","doi-asserted-by":"publisher","award":["T32 GM074904"],"award-info":[{"award-number":["T32 GM074904"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"US National Institutes of Health","doi-asserted-by":"publisher","award":["U54AI117924"],"award-info":[{"award-number":["U54AI117924"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,9,1]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>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 approaches tend to favour 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-maximum-likelihood methods and thus achieves improved operating characteristics in the models considered.<\/jats:p>","DOI":"10.1111\/rssb.12131","type":"journal-article","created":{"date-parts":[[2015,11,9]],"date-time":"2015-11-09T04:04:43Z","timestamp":1447041883000},"page":"781-804","source":"Crossref","is-referenced-by-count":17,"title":["Making the Cut: Improved Ranking and Selection for Large-Scale Inference"],"prefix":"10.1093","volume":"78","author":[{"given":"Nicholas C.","family":"Henderson","sequence":"first","affiliation":[{"name":"University of Wisconsin , Madison , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael A.","family":"Newton","sequence":"additional","affiliation":[{"name":"University of Wisconsin , Madison , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2015,11,8]]},"reference":[{"key":"2023021709311336900_rssb12131-cit-001","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1080\/01621459.1988.10478606","article-title":"A Bayesian approach to ranking and selection of related means with alternatives to analysis-of-variance methodology","volume":"83","author":"Berger","year":"1988","journal-title":"J. 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