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Weighted Rank Regression for Clustered Data Analysis
Author(s) -
Wang YouGan,
Zhao Yudong
Publication year - 2008
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2007.00842.x
Subject(s) - outlier , estimator , mathematics , statistics , weighting , covariance , correlation , regression , covariance matrix , robustness (evolution) , robust statistics , robust regression , regression analysis , rank (graph theory) , biochemistry , chemistry , geometry , combinatorics , gene , radiology , medicine
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.