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

Tian Chen*, Jeanne Kowalski, Rui Chen, Pan Wu, Hui Zhang, Changyong Feng, Xin M. Tu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)3333-3346
Number of pages14
JournalStatistics in Medicine
Issue number19
StatePublished - Aug 30 2016


  • between-subject attribute
  • linear regression
  • rank regression
  • semi-parametric regression models
  • sexual health

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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