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Bayesian quantile regression for ordinal longitudinal data

Author

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  • Rahim Alhamzawi
  • Haithem Taha Mohammad Ali

Abstract

Since the pioneering work by Koenker and Bassett [27], quantile regression models and its applications have become increasingly popular and important for research in many areas. In this paper, a random effects ordinal quantile regression model is proposed for analysis of longitudinal data with ordinal outcome of interest. An efficient Gibbs sampling algorithm was derived for fitting the model to the data based on a location-scale mixture representation of the skewed double-exponential distribution. The proposed approach is illustrated using simulated data and a real data example. This is the first work to discuss quantile regression for analysis of longitudinal data with ordinal outcome.

Suggested Citation

  • Rahim Alhamzawi & Haithem Taha Mohammad Ali, 2018. "Bayesian quantile regression for ordinal longitudinal data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(5), pages 815-828, April.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:5:p:815-828
    DOI: 10.1080/02664763.2017.1315059
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    Citations

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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. Mohammad Arshad Rahman & Angela Vossmeyer, 2019. "Estimation and Applications of Quantile Regression for Binary Longitudinal Data," Advances in Econometrics, in: Ivan Jeliazkov & Justin L. Tobias (ed.), Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B, volume 40, pages 157-191, Emerald Publishing Ltd.
    3. Georges Bresson & Guy Lacroix & Mohammad Arshad Rahman, 0. "Bayesian panel quantile regression for binary outcomes with correlated random effects: an application on crime recidivism in Canada," Empirical Economics, Springer, vol. 0, pages 1-33.
    4. Dries Benoit & Rahim Alhamzawi & Keming Yu, 2013. "Bayesian lasso binary quantile regression," Computational Statistics, Springer, vol. 28(6), pages 2861-2873, December.
    5. Manini Ojha & Mohammad Arshad Rahman, 2020. "Do Online Courses Provide an Equal Educational Value Compared to In-Person Classroom Teaching? Evidence from US Survey Data using Quantile Regression," Papers 2007.06994, arXiv.org.

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