Bayesian analysis of left censored regression with normal-compound gamma priors

Authors

Ahmed Alhamzawi, University of AL-Qadisiyah, College of Science, Department of Mathematics, IraqFollow
Gorgees Shaheed Mohammad, University of AL-Qadisiyah, College of Education, Department of Mathematics, IraqFollow

Document Type

Original Study

Abstract

This paper presents a Bayesian estimation of left censored regression models with scale mixture of normal-compound gamma priors. We presented a new hierarchical modeling for Bayesian inference in left censored regression models. We derived a Gibbs sampling algorithm from this Bayesian hierarchical modeling to estimate the regression parameters with an efficient EM algorithm for updating the hyperparameters. We illustrated the new model using simulation studies and a real data analysis. The results show that the proposed model performs very well in comparison to the other existing models.

Keywords

Tobit regression, normal-compound gamma prior and Gibbs sampler.

Recommended Citation

Alhamzawi, Ahmed and Mohammad, Gorgees Shaheed (2024) "Bayesian analysis of left censored regression with normal-compound gamma priors," Al-Qadisiyah Journal of Pure Science: Vol. 29 : No. 2 , Article 22.
Available at: https://doi.org/10.29350/2411-3514.1304

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