Fadel Hamid Hadi ALHUSSEINI (fadhelfadhel222@yahoo)
University of Craiova, Romania
ABSTRACT
In this paper, we proposed a new hierarchy in Bayesian lasso through using scale mixture uniform (SMU) prior parameters in Tobit quantile regression (Tobit Q Reg) to achieve coefficients estimation and variables selection. SMU is considered a good replacement for scale mixture normal (SMN) to satisfy variable selection in Bayesian lasso (Tobit Q Reg). The Gibbs samplings are derived for all posterior distributions. The performance assessment of the method proposed versus other methods is done through simulation examples and real data.
Keywords: New Bayesian lasso, MCMC, Tobit Quantile Regression, scale mixture uniform , variable selection.
JEL Classification: C11, C51, C52
