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Extending the Mann-Whitney-Wilcoxon rank sum test to longitudinal regression analysis

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  • R. Chen
  • T. Chen
  • N. Lu
  • H. Zhang
  • P. Wu
  • C. Feng
  • X.M. Tu

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.

Suggested Citation

  • R. Chen & T. Chen & N. Lu & H. Zhang & P. Wu & C. Feng & X.M. Tu, 2014. "Extending the Mann-Whitney-Wilcoxon rank sum test to longitudinal regression analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(12), pages 2658-2675, December.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2658-2675
    DOI: 10.1080/02664763.2014.925101
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    1. Jeanne Kowalski, 2004. "Nonparametric inference for stochastic linear hypotheses: Application to high-dimensional data," Biometrika, Biometrika Trust, vol. 91(2), pages 393-408, June.
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