التفاصيل البيبلوغرافية
| العنوان: |
Bayesian composite Tobit quantile regression. |
| المؤلفون: |
Alhusseini, Fadel Hamid Hadi1 (AUTHOR) fadhelfadhel222@yahoo.com, Georgescu, Vasile1 (AUTHOR) |
| المصدر: |
Journal of Applied Statistics. Mar2018, Vol. 45 Issue 4, p727-739. 13p. 6 Charts, 2 Graphs. |
| مصطلحات موضوعية: |
*BAYESIAN analysis, TOBITS, QUANTILE regression, SELECTION theorems, PREDICTION theory |
| مستخلص: |
Composite quantile regression models have been shown to be effective techniques in improving the prediction accuracy [H. Zou and M. Yuan, Composite quantile regressionandthe oraclemodelselection 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. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Business Source Index |
