Bayesian composite Tobit quantile regression |
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Authors: | Fadel Hamid Hadi Alhusseini Vasile Georgescu |
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Institution: | Department of Statistics and Economic Informatics, University of Craiova, Craiova, Romania |
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Abstract: | Composite quantile regression models have been shown to be effective techniques in improving the prediction accuracy H. Zou and M. Yuan, Composite quantile regression and the oracle model selection theory, Ann. Statist. 36 (2008), pp. 1108–1126; J. Bradic, J. Fan, and W. Wang, Penalized composite quasi-likelihood for ultrahighdimensional variable selection, J. R. Stat. Soc. Ser. B 73 (2011), pp. 325–349; Z. Zhao and Z. Xiao, Efficient regressions via optimally combining quantile information, Econometric Theory 30(06) (2014), pp. 1272–1314]. This paper studies composite Tobit quantile regression (TQReg) from a Bayesian perspective. A simple and efficient MCMC-based computation method is derived for posterior inference using a mixture of an exponential and a scaled normal distribution of the skewed Laplace distribution. The approach is illustrated via simulation studies and a real data set. Results show that combine information across different quantiles can provide a useful method in efficient statistical estimation. This is the first work to discuss composite TQReg from a Bayesian perspective. |
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Keywords: | Composite quantile regression MCMC prior distribution posterior inference Tobit quantile regression |
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