{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T11:24:33Z","timestamp":1778239473397,"version":"3.51.4"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T00:00:00Z","timestamp":1690934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Program for Innovative Research Team of Shanghai University of Finance and Economics"},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In the field of multiple hypothesis testing, auxiliary information can be leveraged to enhance the efficiency of test procedures. A common way to make use of auxiliary information is by weighting p-values. However, when the weights are learned from data, controlling the finite-sample false discovery rate becomes challenging, and most existing weighted procedures only guarantee false discovery rate control in an asymptotic limit. In a recent study conducted by Ignatiadis &amp; Huber (2021), a novel \u03c4-censored weighted Benjamini\u2013Hochberg procedure was proposed to control the finite-sample false discovery rate. The authors employed the cross-weighting approach to learn weights for the p-values. This approach randomly splits the data into several folds and constructs a weight for each p-value Pi using the p-values outside the fold containing Pi. Cross-weighting does not exploit the p-value information inside the fold and only balances the weights within each fold, which may result in a loss of power. In this article, we introduce two methods for constructing data-driven weights for \u03c4-censored weighted Benjamini\u2013Hochberg procedures under independence. They provide new insight into masking p-values to prevent overfitting in multiple testing. The first method utilizes a leave-one-out technique, where all but one of the p-values are used to learn a weight for each p-value. This technique masks the information of a p-value in its weight by calculating the infimum of the weight with respect to the p-value. The second method uses partial information from each p-value to construct weights and utilizes the conditional distributions of the null p-values to establish false discovery rate control. Additionally, we propose two methods for estimating the null proportion and demonstrate how to integrate null-proportion adaptivity into the proposed weights to improve power.<\/jats:p>","DOI":"10.1093\/biomet\/asad047","type":"journal-article","created":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T16:48:12Z","timestamp":1690994892000},"page":"479-496","source":"Crossref","is-referenced-by-count":4,"title":["<i>\u03c4<\/i>-censored weighted Benjamini\u2013Hochberg procedures under independence"],"prefix":"10.1093","volume":"111","author":[{"given":"Haibing","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Statistics and Management, Shanghai University of Finance and Economics , 777 Guoding Road , Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huijuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Statistics and Management, Shanghai University of Finance and Economics , 777 Guoding Road , Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,8,2]]},"reference":[{"key":"2024051307573206300_asad047-B1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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