A particle swarm optimization method for variable selection in beta regression model

Beta regression model has received much attention in several science fields in modeling proportions or rates data. Selecting a small subset of relevant variables from a large number of variables is an important task for building a predictive regression model. This paper proposes employing the particle swarm optimization algorithm as a variable selection method in the beta regression model with varying dispersion. The performance of the proposed method is evaluated through simulation and real data application. Results demonstrate the superiority of the proposed method compared to other competitor methods including corrected Akaike information criterion, corrected Schwarz information criterion, and corrected Hannan and Quinn criterion. Thus, the proposed method can efficiently helpful as a variable selection tool in the beta regression model with varying dispersion.