{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T04:12:04Z","timestamp":1773807124574,"version":"3.50.1"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T00:00:00Z","timestamp":1606435200000},"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":[[2021,11,15]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>We introduce a conceptually simple, efficient and easily implemented approach for learning the block structure in a large matrix. Using the properties of U-statistics and large-dimensional random matrix theory, the group structure of many variables can be directly identified based on the eigenvalues and eigenvectors of the scaled sample matrix. We also establish the asymptotic properties of the proposed approach under mild conditions. The finite-sample performance of the approach is examined by extensive simulations and data examples.<\/jats:p>","DOI":"10.1093\/biomet\/asaa099","type":"journal-article","created":{"date-parts":[[2020,11,14]],"date-time":"2020-11-14T12:17:25Z","timestamp":1605356245000},"page":"933-946","source":"Crossref","is-referenced-by-count":8,"title":["Learning block structures in U-statistic-based matrices"],"prefix":"10.1093","volume":"108","author":[{"given":"Weiping","family":"Zhang","sequence":"first","affiliation":[{"name":"International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, Anhui 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baisuo","family":"Jin","sequence":"additional","affiliation":[{"name":"International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, Anhui 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhidong","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin 130024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,11,27]]},"reference":[{"key":"2021111803494521500_B1","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1214\/17-AOS1577","article-title":"Consistency of AIC and BIC in estimating the number of significant components in high-dimensional principal component analysis","volume":"46","author":"Bai,","year":"2018","journal-title":"Ann. 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