Tuning parameter selectors for bridge penalty based on particle swarm optimization method

Abstract

The bridge penalty is widely used as a penalty for selecting and shrinking predictors in regression models. Although its effectiveness is sensitive to the parameters you decide to use for shrinking and adjusting. The shrinkage and tuning parameters of the bridge penalty are chosen concurrently, and a continuous optimization process called particle swarm optimization is proposed as a means to do this. If implemented, the proposed method will greatly facilitate regression modeling with superior prediction performance. The results show that the proposed method is effective in comparison to other well-known methods, but this varies greatly depending on the simulation setup and the real data application.