{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T23:14:55Z","timestamp":1787008495968,"version":"build-2736575974"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R21AG058123"],"award-info":[{"award-number":["R21AG058123"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Resampling techniques have become increasingly popular for estimation of uncertainty. However, data are often fraught with missing values that are commonly imputed to facilitate analysis. This article addresses the issue of using resampling methods such as a jackknife or bootstrap in conjunction with imputations that have been sampled stochastically, in the vein of multiple imputation. We derive the theory needed to illustrate two key points regarding the use of resampling methods in lieu of traditional combining rules. First, imputations should be independently generated multiple times within each replicate group of a jackknife or bootstrap. Second, the number of multiply imputed datasets per replicate group must dramatically exceed the number of replicate groups for a jackknife; however, this is not the case in a bootstrap approach. We also discuss bias-adjusted analogues of the jackknife and bootstrap that are argued to require fewer imputed datasets. A simulation study is provided to support these theoretical conclusions.<\/jats:p>","DOI":"10.1093\/biomet\/asaf059","type":"journal-article","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T11:41:52Z","timestamp":1753443712000},"source":"Crossref","is-referenced-by-count":3,"title":["Resampling methods with multiply imputed data"],"prefix":"10.1093","volume":"112","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4972-1733","authenticated-orcid":false,"given":"Michael W","family":"Robbins","sequence":"first","affiliation":[{"name":"RAND Corporation , 4570 Fifth Avenue #600 , Pittsburgh, Pennsylvania 15213,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lane","family":"Burgette","sequence":"additional","affiliation":[{"name":"RAND Corporation , 1200 South Hayes Street , Arlington, Virginia 22202,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,7,30]]},"reference":[{"key":"2026032607102077800_asaf059-B1","first-page":"3533","article-title":"Bootstrap inference for multiple imputation under uncongeniality and misspecification","volume":"29","author":"Bartlett","year":"2020","journal-title":"Statist. 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