Bayesian Nonlinear Quantile Regression Approach for Longitudinal Ordinal Data

Hang Yang , Zhuojian Chen , Weiping Zhang

Communications in Mathematics and Statistics ›› 2019, Vol. 7 ›› Issue (2) : 123 -140.

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Communications in Mathematics and Statistics ›› 2019, Vol. 7 ›› Issue (2) : 123 -140. DOI: 10.1007/s40304-018-0148-7
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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.

Keywords

Ordinal longitudinal data / Bayesian approach / Quantile regression / MCMC / Metropolis–Hastings algorithm

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Hang Yang,Zhuojian Chen,Weiping Zhang. Bayesian Nonlinear Quantile Regression Approach for Longitudinal Ordinal Data. Communications in Mathematics and Statistics, 2019, 7(2): 123-140 DOI:10.1007/s40304-018-0148-7

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Funding

the National Key Research and Development Plan(2016YFC0800100)

National Natural Science Foundation of China(11671374)

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