Please use this identifier to cite or link to this item: https://doi.org/10.1111/j.1541-0420.2007.00842.x
Title: Weighted rank regression for clustered data analysis
Authors: Wang, Y.-G.
Zhao, Y. 
Keywords: Clustered data
Covariance estimation
Dependent data
Estimating functions
Longitudinal data
Rank estimation
Repeated measures
Wilcoxon score
Issue Date: Mar-2008
Source: Wang, Y.-G., Zhao, Y. (2008-03). Weighted rank regression for clustered data analysis. Biometrics 64 (1) : 39-45. ScholarBank@NUS Repository. https://doi.org/10.1111/j.1541-0420.2007.00842.x
Abstract: 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. © 2007, The International Biometric Society.
Source Title: Biometrics
URI: http://scholarbank.nus.edu.sg/handle/10635/130446
ISSN: 0006341X
DOI: 10.1111/j.1541-0420.2007.00842.x
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