Bayesian bridge quantile regression |
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Authors: | Rahim Alhamzawi Zakariya Yahya Algamal |
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Affiliation: | 1. Department of Statistics, College of Administration and Economics, University of Al-Qadisiyah, Al-Qadisiyah, Iraq;2. Department of Statistics and Informatics, University of Mosul, Mosul, Iraq |
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Abstract: | Regularization methods for simultaneous variable selection and coefficient estimation have been shown to be effective in quantile regression in improving the prediction accuracy. In this article, we propose the Bayesian bridge for variable selection and coefficient estimation in quantile regression. A simple and efficient Gibbs sampling algorithm was developed for posterior inference using a scale mixture of uniform representation of the Bayesian bridge prior. This is the first work to discuss regularized quantile regression with the bridge penalty. Both simulated and real data examples show that the proposed method often outperforms quantile regression without regularization, lasso quantile regression, and Bayesian lasso quantile regression. |
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Keywords: | Bayesian inference MCMC Qantile regression Skewed Laplace distribution |
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