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

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  • Jang, Woosung
  • Wang, Huixia Judy

Abstract

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 theory. Through joint-quantile modeling, the proposed algorithm can yield the joint posterior distribution of quantile coefficients at multiple quantiles and meanwhile avoid the quantile crossing issue. The finite sample performance of the proposed method is assessed through a simulation study and the analysis of an apnea duration data.

Suggested Citation

  • Jang, Woosung & Wang, Huixia Judy, 2015. "A semiparametric Bayesian approach for joint-quantile regression with clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 99-115.
    • Handle: RePEc:eee:csdana:v:84:y:2015:i:c:p:99-115
    DOI: 10.1016/j.csda.2014.11.008
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    References listed on IDEAS

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    Cited by:

    1. Jayabrata Biswas & Pulak Ghosh & Kiranmoy Das, 2020. "A semi-parametric quantile regression approach to zero-inflated and incomplete longitudinal outcomes," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 261-283, June.
    2. Hemant Kulkarni & Jayabrata Biswas & Kiranmoy Das, 2019. "A joint quantile regression model for multiple longitudinal outcomes," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(4), pages 453-473, December.
    3. Priya Kedia & Damitri Kundu & Kiranmoy Das, 2023. "A Bayesian variable selection approach to longitudinal quantile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 149-168, March.

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