A Bayesian Approach to Binary Logistic Regression Model with Application to OECD Data

Authors : Asuman YILMAZ, Heray ÇELİK

Pages : 94-101

Doi:10.53433/yyufbed.837533

View : 20 | Download : 8

Publication Date : 2021-08-31

Article Type : Research Paper

Abstract :In spite of being a common method for estimating the model parameters, Maximum Likelihood insert ignore into journalissuearticles values(ML); method may give bias results for small sample sizes. To overcome this problem, Bayesian method is usually utilized to obtain the estimates of the model parameters as an alternative to the ML method. In this study, a real data set was analyzed by using the binary logistic regression model. Parameters of the binary logistic regression model were estimated by using ML and Bayesian methods. Modeling performance of the binary logistics regression model based on the Bayesian estimates was compared with the model based on the ML estimates. Well-known information criteria such as AIC and BIC were used in this comparison.
Keywords : Binary Logistic regression, Maximum likelihood, Bayesian method