Weighted rank regression for clustered data analysis |
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Authors: | Wang You-Gan Zhao Yudong |
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Institution: | CSIRO Mathematical and Information Sciences, CSIRO Long Pocket Laboratories, 120 Meiers Road, Indooroopilly, Queensland 4068, Australia email:; and Department of Statistics and Applied Probability, National University of Singapore, 3 Science Drive 2, Singapore 117546 email: |
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Abstract: | Summary . We consider ranked-based regression models for clustered data analysis. A weighted Wilcoxon rank method is proposed to take account of within-cluster correlations and varying cluster sizes. The asymptotic normality of the resulting estimators is established. A method to estimate covariance of the estimators is also given, which can bypass estimation of the density function. Simulation studies are carried out to compare different estimators for a number of scenarios on the correlation structure, presence/absence of outliers and different correlation values. The proposed methods appear to perform well, in particular, the one incorporating the correlation in the weighting achieves the highest efficiency and robustness against misspecification of correlation structure and outliers. A real example is provided for illustration. |
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Keywords: | Clustered data Covariance estimation Dependent data Estimating functions Longitudinal data Rank estimation Repeated measures Wilcoxon score |
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