{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T07:20:06Z","timestamp":1791444006874,"version":"4.3.3"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T00:00:00Z","timestamp":1598400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,15]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>Posterior computation for high-dimensional data with many parameters can be challenging. This article focuses on a new method for approximating posterior distributions of a low- to moderate-dimensional parameter in the presence of a high-dimensional or otherwise computationally challenging nuisance parameter. The focus is on regression models and the key idea is to separate the likelihood into two components through a rotation. One component involves only the nuisance parameters, which can then be integrated out using a novel type of Gaussian approximation. We provide theory on approximation accuracy that holds for a broad class of forms of the nuisance component and priors. Applying our method to simulated and real datasets shows that it can outperform state-of-the-art posterior approximation approaches.<\/jats:p>","DOI":"10.1093\/biomet\/asaa068","type":"journal-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T19:09:33Z","timestamp":1597950573000},"page":"269-282","source":"Crossref","is-referenced-by-count":2,"title":["Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation"],"prefix":"10.1093","volume":"108","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1777-3857","authenticated-orcid":false,"given":"W","family":"van den Boom","sequence":"first","affiliation":[{"name":"Yale-NUS College, National University of Singapore, 16 College Avenue West #01-220, Singapore 138527, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G","family":"Reeves","sequence":"additional","affiliation":[{"name":"Department of Statistical Science, Duke University, Box 90251, Durham, North Carolina 27708, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D B","family":"Dunson","sequence":"additional","affiliation":[{"name":"Department of Statistical Science, Duke University, Box 90251, Durham, North Carolina 27708, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,8,26]]},"reference":[{"key":"2021101517140075600_B1","doi-asserted-by":"crossref","first-page":"4709","DOI":"10.1038\/sj.emboj.7600481","article-title":"The E2F family: Specific functions and overlapping interests","volume":"23","author":"Attwooll,","year":"2004","journal-title":"EMBO J."},{"key":"2021101517140075600_B2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1214\/ss\/1009211804","article-title":"Integrated likelihood methods for eliminating nuisance parameters","volume":"14","author":"Berger,","year":"1999","journal-title":"Statist. 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