Constrained S-estimators for linear mixed effects models with covariance components
Article first published online: 13 JAN 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 30, Issue 14, pages 1735–1750, 30 June 2011
How to Cite
Chervoneva, I. and Vishnyakov, M. (2011), Constrained S-estimators for linear mixed effects models with covariance components. Statist. Med., 30: 1735–1750. doi: 10.1002/sim.4169
- Issue published online: 2 JUN 2011
- Article first published online: 13 JAN 2011
- Manuscript Accepted: 12 NOV 2010
- Manuscript Received: 28 MAY 2010
- multivariate linear mixed effects models;
- robust estimation;
- CTBS estimator for LME model;
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-estimators were considered before for robust estimation of the LME models, and recently a constrained S-estimator was proposed. This S-estimator 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-estimator 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-estimators. Performance of the S-estimators 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. Copyright © 2011 John Wiley & Sons, Ltd.