Model Selection in Beta Regression Analysis Using Several Information Criteria and Heuristic Optimization
Emre Dünder, Mehmet Ali Cengiz · DergiPark (Istanbul University) · 2020
In the context of generalized linear modeling (GLM), the beta regression analysis is used to estimate regression models when the dependent variable lies between (0,1).In this paper, we carried out a model selection process using several information criteria with heuristic optimization.We employed the differential evolution algorithm as a heuristic optimization method to select the best model for beta regression analysis.The results show that the alternative-type information criteria provide competitive results during the model selection process in beta regression analysis.