Rank-preserving regression: a more robust rank regression model against outliers

• Published in: Statistics in medicine Vol. 35; no. 19; pp. 3333 - 3346
• Main Authors: Chen, Tian, Kowalski, Jeanne, Chen, Rui, Wu, Pan, Zhang, Hui, Feng, Changyong, Tu, Xin M.
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 30.08.2016; Wiley Subscription Services, Inc
• Subjects: between-subject attribute; Computer Simulation; Data analysis; Humans; linear regression; Longitudinal Studies; Mathematical models; Models, Statistical; Parameter estimation; rank regression; Regression Analysis; semi-parametric regression models; sexual health; Simulation; between-subject attribute; linear regression; sexual health; rank regression; semi-parametric regression models
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.6930

Mean‐based semi‐parametric regression models such as the popular generalized estimating equations are widely used to improve robustness of inference over parametric models. Unfortunately, such models are quite sensitive to outlying observations. The Wilcoxon‐score‐based rank regression (RR) provides more robust estimates over generalized estimating equations against outliers. However, the RR and its extensions do not sufficiently address missing data arising in longitudinal studies. In this paper, we propose a new approach to address outliers under a different framework based on the functional response models. This functional‐response‐model‐based alternative not only addresses limitations of the RR and its extensions for longitudinal data, but, with its rank‐preserving property, even provides more robust estimates than these alternatives. The proposed approach is illustrated with both real and simulated data. Copyright © 2016 John Wiley & Sons, Ltd.