Bayesian adaptive Lasso Tobit regression

Authors

  • Haider Kadhim Abbas Department of Statistics, College of Administration and Economics, University of Al-Qadisiyah, Iraq.
  • Rahim Jabbar Thaher Department of Statistics, College of Administration and Economics, University of Al-Qadisiyah, Iraq.

DOI:

https://doi.org/10.29304/jqcm.2019.11.1.471

Keywords:

Tobit regression; Bayesian adaptive Lasso Tobit regression (BALTR); Variable selection (VS).

Abstract

      In this paper, we introduce a new procedure for model selection in Tobit regression, we suggest the Bayesian adaptive Lasso Tobit regression (BALTR) for variable selection (VS) and coefficient estimation.  We submitted a Bayesian hierarchical model and Gibbs sampler (GS) for our procedure. Our proposed procedure is clarified by means of simulations and a real data analysis. Results demonstrate our procedure performs well in comparison to further procedures.

 

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Published

2019-01-25

How to Cite

Kadhim Abbas, H., & Jabbar Thaher, R. (2019). Bayesian adaptive Lasso Tobit regression. Journal of Al-Qadisiyah for Computer Science and Mathematics, 11(1), Stat Page 1 – 10. https://doi.org/10.29304/jqcm.2019.11.1.471

Issue

Vol. 11 No. 1 (2019): JQCM

Section

Math Articles