A Monte Carlo study of REML and robust rank-based analyses for the random intercept mixed model |
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Authors: | Hend A. Auda John D. Kloke Mahmoud Sadek |
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Affiliation: | 1. Department of Commerce and Statistics, Helwan University, Cairo, Egypt;2. Department of Biostatistics, University of Wisconsin, Madison, WI |
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Abstract: | Restricted maximum likelihood (REML) methods are traditionally used for analyzing mixed models. Based on a multivariate normal likelihood, these analyses are sensitive to outliers. Recently developed robust rank-based procedures offer a complete analysis of mixed model: estimation of fixed effects, standard errors, and estimation of variance components. The results of a large Monte Carlo study are presented, comparing these two analyses for many situations over multivariate normal and contaminated normal distributions. The rank-based analyses are much more powerful and efficient than the REML analyses over all non-normal situations, while losing little power for normal errors. |
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Keywords: | Cluster correlated data Nonparametrics Wilcoxon procedures |
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