{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T08:00:46Z","timestamp":1793174446488,"version":"4.5.0"},"reference-count":19,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:00:00Z","timestamp":1728345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The false discovery rate is a commonly used criterion in multiple testing, and the Benjamini\u2013Hochberg procedure is a standard approach to false discovery rate control. To increase its power, adaptive Benjamini\u2013Hochberg procedures, that use estimates of the null proportion, have been proposed. A particularly popular approach being that based on Storey\u2019s estimator. The performance of Storey\u2019s estimator hinges on a critical hyperparameter, such that a pre-fixed configuration may lack power and existing data-driven hyperparameters may compromise false discovery rate control. In this work, we propose a novel class of adaptive hyperparameters and establish the false discovery rate control of the associated adaptive Benjamini\u2013Hochberg procedure using a martingale argument. Within this class of data-driven hyperparameters, we further present a specific configuration designed to maximize the number of rejections and characterize its convergence to the optimal hyperparameter under a mixture model. The proposed method exhibits significant power gains, particularly in cases with a conservative null distribution, which are common in composite null testing, or with a moderate proportion of weak nonnulls, as is typically observed in biological experiments with enrichment processes.<\/jats:p>","DOI":"10.1093\/biomet\/asae051","type":"journal-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T16:11:25Z","timestamp":1728403885000},"source":"Crossref","is-referenced-by-count":3,"title":["An adaptive null proportion estimator for false discovery rate control"],"prefix":"10.1093","volume":"112","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4863-1656","authenticated-orcid":false,"given":"Zijun","family":"Gao","sequence":"first","affiliation":[{"name":"Marshall Business School, University of Southern California , 3670 Trousdale Parkway , Los Angeles, California 90089,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,10,8]]},"reference":[{"key":"2025012017175904300_asae051-B1","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1214\/22-AOS2244","article-title":"Testing for outliers with conformal p-values","volume":"51","author":"Bates","year":"2023","journal-title":"Ann. 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