Bayesian Adaptive Bridge Regression for Ordinal Models with an Application

Dhiyaa Hazem Qasem  Al-Jabri; Rahim Jabbar  Al-Hamzawi

doi:10.24996/ijs.2020.SI.1.22

Authors

Dhiyaa Hazem Qasem Al-Jabri Department of Statistics, College of Administration and Economics, University of Al-Qadisiyah, Iraq
Rahim Jabbar Al-Hamzawi Department of Statistics, College of Administration and Economics, University of Al-Qadisiyah, Iraq

DOI:

https://doi.org/10.24996/ijs.2020.SI.1.22

Keywords:

Bayesian Bridge, Variable Selection, Ordinal Model, Scale Mixture Of Uniform

Abstract

In this article, we propose a Bayesian Adaptive bridge regression for ordinal model. We developed a new hierarchical model for ordinal regression in the Bayesian adaptive bridge. We consider a fully Bayesian approach that yields a new algorithm with tractable full conditional posteriors. All of the results in real data and simulation application indicate that our method is effective and performs very good compared to other methods. We can also observe that the estimator parameters in our proposed method, compared with other methods, are very close to the true parameter values.