{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T14:30:46Z","timestamp":1769783446594,"version":"3.49.0"},"reference-count":24,"publisher":"Wiley","issue":"14","license":[{"start":{"date-parts":[[2011,1,13]],"date-time":"2011-01-13T00:00:00Z","timestamp":1294876800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2011,1,13]],"date-time":"2011-01-13T00:00:00Z","timestamp":1294876800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"},{"start":{"date-parts":[[2011,1,13]],"date-time":"2011-01-13T00:00:00Z","timestamp":1294876800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2011,6,30]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Linear mixed effects (LME) models are increasingly used for analyses of biological and biomedical data. When the multivariate normal assumption is not adequate for an LME model, then a robust estimation approach is preferable to the maximum likelihood one. M\u2010estimators were considered before for robust estimation of the LME models, and recently a constrained S\u2010estimator was proposed. This S\u2010estimator cannot be applied directly to LME models with correlated error terms and vector random effects with correlated dimensions. Therefore, a modification is proposed, which extends application of the constrained S\u2010estimator to the LME models for multivariate responses with correlated dimensions and to longitudinal data. Also, a new computational algorithm is developed for computing constrained S\u2010estimators. Performance of the S\u2010estimators based on the original Tukey's biweight and translated biweight is evaluated in a small simulation study with repeated multivariate responses with correlated dimensions. The proposed methodology is applied to jointly analyze repeated measures on three cholesterol components, HDL, LDL, and triglycerides. 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