{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T03:02:00Z","timestamp":1790650920763,"version":"4.1.0"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T00:00:00Z","timestamp":1651708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Hong Kong Research Grants Council"},{"DOI":"10.13039\/501100004853","name":"The Chinese University of Hong Kong","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004853","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,2,11]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>We propose a linearized maximum rank correlation estimator for the single-index model. Unlike the existing maximum rank correlation and other rank-based methods, the proposed estimator has a closed-form expression, making it appealing in theory and computation. The proposed estimator is robust to outliers in the response and its construction does not need knowledge of the unknown link function or the error distribution. Under mild conditions, it is shown to be consistent and asymptotically normal when the predictors satisfy the linearity of the expectation assumption. A more general class of estimators is also studied. Inference procedures based on the plug-in rule or random weighting resampling are employed for variance estimation. The proposed method can be easily modified to accommodate censored data. It can also be extended to deal with high-dimensional data combined with a penalty function. Extensive simulation studies provide strong evidence that the proposed method works well in various practical situations. Its application is illustrated with the Beijing PM 2.5 dataset.<\/jats:p>","DOI":"10.1093\/biomet\/asac027","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T11:16:07Z","timestamp":1651576567000},"page":"187-203","source":"Crossref","is-referenced-by-count":9,"title":["Linearized maximum rank correlation estimation"],"prefix":"10.1093","volume":"110","author":[{"given":"Guohao","family":"Shen","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics, The Hong Kong Polytechnic University , Hung Hom, Kowloon, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kani","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Hong Kong University of Science and Technology , Clear Water Bay, Kowloon, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5218-9269","authenticated-orcid":false,"given":"Jian","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Statistics and Actuarial Science, University of Iowa , 241 Schaeffer Hall, Iowa City, Iowa 52242-1409, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1293-1040","authenticated-orcid":false,"given":"Yuanyuan","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Statistics, The Chinese University of Hong Kong , Shatin, New Territories, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,5,5]]},"reference":[{"key":"2023021111211835100_B1","doi-asserted-by":"crossref","first-page":"2118","DOI":"10.1214\/09-AOS752","article-title":"Optimal rates of convergence for covariance matrix estimation","volume":"38","author":"Cai,","year":"2010","journal-title":"Ann. 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