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Bayesian Nonlinear Quantile Regression Approach for Longitudinal Ordinal Data

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Abstract

Longitudinal data with ordinal outcomes commonly arise in clinical and social studies, where the purpose of interest is usually quantile curves rather than a simple reference range. In this paper we consider Bayesian nonlinear quantile regression for longitudinal ordinal data through a latent variable. An efficient Metropolis–Hastings within Gibbs algorithm was developed for model fitting. Simulation studies and a real data example are conducted to assess the performance of the proposed method. Results show that the proposed approach performs well.

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Acknowledgements

The authors are grateful to the editor and the anonymous referees for their helpful comments and suggestions, which have helped us produce a substantially improved version.

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Authors and Affiliations

  1. Department of Statistics and Finance, University of Science and Technology of China, Hefei, China

    Hang Yang, Zhuojian Chen & Weiping Zhang

Authors
  1. Hang Yang
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  2. Zhuojian Chen
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  3. Weiping Zhang
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Corresponding author

Correspondence to Weiping Zhang.

Additional information

This work is supported in part by the National Key Research and Development Plan (No. 2016YFC0800100) and National Natural Science Foundation of China Grant 11671374 and 71631006.

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Yang, H., Chen, Z. & Zhang, W. Bayesian Nonlinear Quantile Regression Approach for Longitudinal Ordinal Data. Commun. Math. Stat. 7, 123–140 (2019). https://doi.org/10.1007/s40304-018-0148-7

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  • DOI: https://doi.org/10.1007/s40304-018-0148-7

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