Open AccessBayesian Adaptive Bridge Regression for Ordinal Models with an Application
Author(s) -
Dhiyaa Hazem Qasem Al-Jabri,
Rahim Alhamzawi
Publication year - 2020
Publication title -
iraqi journal of science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.152
H-Index - 4
eISSN - 2312-1637
pISSN - 0067-2904
DOI - 10.24996/ijs.2020.si.1.22
Subject(s) - ordinal regression , bayesian probability , estimator , computer science , bridge (graph theory) , bayesian linear regression , regression , ordinal data , variable order bayesian network , regression analysis , artificial intelligence , algorithm , bayesian inference , machine learning , data mining , statistics , mathematics , medicine
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.