A semiparametric Bayesian approach for joint-quantile regression with clustered data

@article{Jang2015ASB,
  title={A semiparametric Bayesian approach for joint-quantile regression with clustered data},
  author={Woosung Jang and Huixia Judy Wang},
  journal={Computational Statistics & Data Analysis},
  year={2015},
  volume={84},
  pages={99-115}
}
Based on a semiparametric Bayesian framework, a joint-quantile regression method is developed for analyzing clustered data, where random effects are included to accommodate the intra-cluster dependence. Instead of posing any parametric distributional assumptions on the random errors, the proposed method approximates the central density by linearly interpolating the conditional quantile functions of the response at multiple quantiles and estimates the tail densities by adopting extreme value… CONTINUE READING