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Quantile Regression in Biostatistics

Author

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  • Jayabrata Biswas

    (Interdisciplinary Statistical Research Unit, Indian Statistical Institute, India)

  • Hemant Kulkarni

    (Human Genetics Unit, Indian Statistical Institute, India)

  • Kiranmoy Das

    (Interdisciplinary Statistical Research Unit, Indian Statistical Institute, India)

Abstract

Quantile regression has been a very effective modelling approach in many real applications. Unlike the mean regression, quantile regression focuses on modelling the entire distribution of the response variable, not just the mean value. We review some working models of quantile regression. We demonstrate some real applications of such modelling in Biostatistics and related disciplines. We also discuss some recent developments in modelling multiple responses in the context of quantile regression.

Suggested Citation

  • Jayabrata Biswas & Hemant Kulkarni & Kiranmoy Das, 2017. "Quantile Regression in Biostatistics," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 2(5), pages 102-105, August.
    • Handle: RePEc:adp:jbboaj:v:2:y:2017:i:5:p:102-105
    DOI: 10.19080/BBOAJ.2017.02.555596
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    References listed on IDEAS

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    2. Tershakovec, A.M. & Shannon, B.M. & Achterberg, C.L. & McKenzie, J.M. & Martel, J.K. & Smiciklas-Wright, H. & Pammer, S.E. & Cortner, J.A., 1998. "One-year follow-up of nutrition education for hypercholesterolemic children," American Journal of Public Health, American Public Health Association, vol. 88(2), pages 258-261.
    3. T. J. Cole, 1988. "Fitting Smoothed Centile Curves to Reference Data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 151(3), pages 385-406, May.
    4. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
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