{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T23:07:37Z","timestamp":1788908857343,"version":"build-2803163510"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026,7,6]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>This article focuses on covariance estimation for multi-view data. Popular approaches rely on factor-analytic decompositions with shared and view-specific latent factors. Posterior computation is conducted via expensive and brittle Markov chain Monte Carlo sampling or variational approximations that underestimate uncertainty and lack theoretical guarantees. Our proposed methodology employs spectral decompositions to estimate and align latent factors that are active in at least one view. Conditionally on these factors, we choose jointly conjugate prior distributions for factor loadings and residual variances. The resulting posterior is a simple product of normal-inverse gamma distributions for each variable, bypassing Markov chain Monte Carlo and facilitating posterior computation. We prove favourable increasing-dimension asymptotic properties, including posterior contraction and central limit theorems for point estimators. We show excellent performance in simulations, including accurate uncertainty quantification, and apply the methodology to integrate four high-dimensional views from a multi-omics dataset of cancer cell samples.<\/jats:p>","DOI":"10.1093\/biomet\/asag038","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T16:37:09Z","timestamp":1781627829000},"source":"Crossref","is-referenced-by-count":0,"title":["Inference on covariance structure in high-dimensional multi-view data"],"prefix":"10.1093","volume":"113","author":[{"given":"L","family":"Mauri","sequence":"first","affiliation":[{"name":"Department of Statistical Science, Duke University , Box 90251, Durham, North Carolina 27707,","place":["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 27707, \u00a0 dunson@duke.edu","place":["U.S.A"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,6,18]]},"reference":[{"key":"2026090818351928900_asag038-B1","doi-asserted-by":"crossref","first-page":"3887","DOI":"10.1038\/ncomms4887","article-title":"A pan-cancer proteomic perspective on The Cancer Genome Atlas","volume":"5","author":"Akbani","year":"2014","journal-title":"Nature Commun"},{"key":"2026090818351928900_asag038-B2","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/s13072-018-0205-1","article-title":"Consistent inverse correlation between DNA methylation of the first intron and gene expression across tissues and species","volume":"11","author":"Anastasiadi","year":"2018","journal-title":"Epigenetics Chromatin"},{"key":"2026090818351928900_asag038-B3","author":"Anceschi","year":"2026"},{"key":"2026090818351928900_asag038-B4","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1186\/s13059-020-02015-1","article-title":"MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data","volume":"21","author":"Argelaguet","year":"2020","journal-title":"Genome Biol"},{"key":"2026090818351928900_asag038-B5","doi-asserted-by":"crossref","first-page":"e8124","DOI":"10.15252\/msb.20178124","article-title":"Multi-omics factor analysis\u2014a framework for unsupervised integration of multi-omics data sets","volume":"14","author":"Argelaguet","year":"2018","journal-title":"Mol. 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