Weighted rank regression for clustered data analysis

Wang, You-Gan & Zhao, Yudong (2008) Weighted rank regression for clustered data analysis. Biometrics, 64(1), pp. 39-45.

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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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10 citations in Scopus
8 citations in Web of Science®
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ID Code: 90456
Item Type: Journal Article
Refereed: Yes
Keywords: clustered data, covariance estimation, dependent data, estimating, functions, longitudinal data, rank estimation, repeated measures, Wilcoxon score, size
DOI: 10.1111/j.1541-0420.2007.00842.x
ISSN: 0006-341X
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 17 Nov 2015 02:59
Last Modified: 17 Nov 2015 02:59

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