 Extending the Mann-Whitney-Wilcoxon rank sum test to longitudinal regression analysisR. Chen, T. Chen, N. Lu, H. Zhang, P. Wu, C. Feng and X.M. Tu Journal of Applied Statistics, 2014, vol. 41, issue 12, 2658-2675 Abstract: Outliers are commonly observed in psychosocial research, generally resulting in biased estimates when comparing group differences using popular mean-based models such as the analysis of variance model. Rank-based methods such as the popular Mann-Whitney-Wilcoxon (MWW) rank sum test are more effective to address such outliers. However, available methods for inference are limited to cross-sectional data and cannot be applied to longitudinal studies under missing data. In this paper, we propose a generalized MWW test for comparing multiple groups with covariates within a longitudinal data setting, by utilizing the functional response models. Inference is based on a class of U-statistics-based weighted generalized estimating equations, providing consistent and asymptotically normal estimates not only under complete but missing data as well. The proposed approach is illustrated with both real and simulated study data. Date: 2014 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:41:y:2014:i:12:p:2658-2675 Ordering information: This journal article can be ordered from DOI: 10.1080/02664763.2014.925101 Access Statistics for this article Journal of Applied Statistics is currently edited by Robert Aykroyd More articles in Journal of Applied Statistics from Taylor & Francis Journals |
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