New Bayesian Lasso in Tobit Quantile Regression

Fadel Hamid Hadi Alhusseini
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Fadel Hamid Hadi Alhusseini: University of Craiova, Romania

Romanian Statistical Review Supplement, 2017, vol. 65, issue 6, 213-229

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 (search for similar items in EconPapers)
JEL-codes: C11 C51 C52 (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:rsr:supplm:v:65:y:2017:i:6:p:213-229

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