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Rank-based regression with repeated measurements data

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  • Sin-Ho Jung

Abstract

A rank-based regression method is proposed for repeated measurements data. It is a generalisation of the classical Wilcoxon--Mann--Whitney rank statistic for independent observations. The method is valid under a weak condition on the error terms that can accommodate certain heteroscedasticity and within-subject dependency. The asymptotic normality of the proposed estimator is proved using empirical process theory. A variance estimator, shown to be consistent, is also constructed. The proposed method is illustrated using data from a clinical trial on treating labour pain. Robustness and efficiency of the estimator is demonstrated in simulation studies. Copyright Biometrika Trust 2003, Oxford University Press.

Suggested Citation

  • Sin-Ho Jung, 2003. "Rank-based regression with repeated measurements data," Biometrika, Biometrika Trust, vol. 90(3), pages 732-740, September.
    • Handle: RePEc:oup:biomet:v:90:y:2003:i:3:p:732-740
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    Citations

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    Cited by:

    1. You-Gan Wang & Xu Lin & Min Zhu, 2005. "Robust Estimating Functions and Bias Correction for Longitudinal Data Analysis," Biometrics, The International Biometric Society, vol. 61(3), pages 684-691, September.
    2. Liya Fu & You-Gan Wang, 2012. "Efficient Estimation for Rank-Based Regression with Clustered Data," Biometrics, The International Biometric Society, vol. 68(4), pages 1074-1082, December.
    3. Fu, Liya & Wang, You-Gan, 2016. "Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 492-502.
    4. Tianqing Liu & Xiaohui Yuan, 2020. "Empirical likelihood-based weighted rank regression with missing covariates," Statistical Papers, Springer, vol. 61(2), pages 697-725, April.
    5. Lan Wang & Runze Li, 2009. "Weighted Wilcoxon-Type Smoothly Clipped Absolute Deviation Method," Biometrics, The International Biometric Society, vol. 65(2), pages 564-571, June.
    6. Fu, Liya & Wang, You-Gan & Bai, Zhidong, 2010. "Rank regression for analysis of clustered data: A natural induced smoothing approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1036-1050, April.
    7. Xiaoming Lu & Zhaozhi Fan, 2015. "Weighted quantile regression for longitudinal data," Computational Statistics, Springer, vol. 30(2), pages 569-592, June.
    8. Fu, Liya & Wang, You-Gan, 2012. "Quantile regression for longitudinal data with a working correlation model," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2526-2538.
    9. Xiaoming Lu & Zhaozhi Fan, 2020. "Generalized linear mixed quantile regression with panel data," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-16, August.
    10. You-Gan Wang & Yudong Zhao, 2008. "Weighted Rank Regression for Clustered Data Analysis," Biometrics, The International Biometric Society, vol. 64(1), pages 39-45, March.
    11. Lin, Huazhen & Li, Yi & Tan, Ming T., 2013. "Estimating a unitary effect summary based on combined survival and quantitative outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 129-139.

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