Variable selection in beta regression model using firefly algorithm - Mohammed Taher, Zahraa (Statistics, Optimization & Information Computing, 2025)
The Beta regression model presents widespread scientific interest when used for modeling both proportions and rates data. Creating a predictive regression model requires the identification of select important variables from abundant available options. This work introduces the use of a firefly algorithm for selecting variables when applying the beta regression model featuring varying dispersion parameters. Evaluation of the proposed method’s performance takes place through simulations and real data implementation. The proposed method demonstrates better performance than corrected Akaike information criterion, corrected Schwarz information criterion, and corrected Hannan and Quinn criterion in results analysis. The proposed method functions effectively to select variables in beta regression models which have varying dispersion levels.