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Bayesian analysis of penalized quantile regression for longitudinal data

Author

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  • A. Aghamohammadi

    (University of Zanjan)

  • S. Mohammadi

    (University of Zanjan)

Abstract

This paper considers penalized quantile regression model for random effects longitudinal data from a Bayesian perspective. The introduction of a large number of individual random effects can significantly inflate the variability of estimates of other covariate effects. To modify this inflation effect a hierarchical Bayesian model is introduced to shrink the individual effects toward the common population values by using the Lasso and adaptive Lasso penalties in the quantile regression check function. A Gibbs sampling algorithm is developed to simulate the parameters from the posterior distributions. The simulation studies and real data analysis indicate that the proposed methods generally perform better in comparison to the other approaches.

Suggested Citation

  • A. Aghamohammadi & S. Mohammadi, 2017. "Bayesian analysis of penalized quantile regression for longitudinal data," Statistical Papers, Springer, vol. 58(4), pages 1035-1053, December.
    • Handle: RePEc:spr:stpapr:v:58:y:2017:i:4:d:10.1007_s00362-015-0737-4
    DOI: 10.1007/s00362-015-0737-4
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    References listed on IDEAS

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    Cited by:

    1. Siamak Ghasemzadeh & Mojtaba Ganjali & Taban Baghfalaki, 2018. "Bayesian quantile regression for analyzing ordinal longitudinal responses in the presence of non-ignorable missingness," METRON, Springer;Sapienza Università di Roma, vol. 76(3), pages 321-348, December.
    2. Yu-Zhu Tian & Man-Lai Tang & Wai-Sum Chan & Mao-Zai Tian, 2021. "Bayesian bridge-randomized penalized quantile regression for ordinal longitudinal data, with application to firm’s bond ratings," Computational Statistics, Springer, vol. 36(2), pages 1289-1319, June.
    3. Chang-Sheng Liu & Han-Ying Liang, 2023. "Bayesian empirical likelihood of quantile regression with missing observations," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(3), pages 285-313, April.
    4. Xiaowen Dai & Libin Jin & Lei Shi, 2023. "Quantile regression in random effects meta-analysis model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 469-492, June.
    5. Christian E. Galarza & Luis M. Castro & Francisco Louzada & Victor H. Lachos, 2020. "Quantile regression for nonlinear mixed effects models: a likelihood based perspective," Statistical Papers, Springer, vol. 61(3), pages 1281-1307, June.

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