Developing a Semi-parametric Zero-Inflated Beta Regression Model Using P-splines: Simulation and Application

Abstract

Analyzing proportional data with excessive zeros and complex relationships presents a significant challenge in various fields. To address this, we propose a developing semiparametric Zero-Inflated Beta Regression (ZIBE) model incorporating P-splines. Our ZIBE.pb model offers a unique combination of flexibility and interpretability, allowing for the modeling of non-linear relationships and the identification of factors contributing to zero inflation. Extensive simulations demonstrate the ZIBE.pb model's superior model fit and predictive accuracy compared to existing parametric models. The proposed estimators exhibit minimum GD, AIC, BIC, and MSE, as confirmed by Monte Carlo simulation studies and real-world applications. Our ZIBE.pb model has broad applications in various fields, including political science, economics, and social sciences. To demonstrate its utility, we applied it to the Varieties of Democracy (V-Dem) dataset. In conclusion, our ZIBE.pb model offers a robust and versatile tool for analyzing proportional data with excessive zeros and complex relationships. Its ability to capture both linear and nonlinear effects, coupled with its interpretability, makes it a valuable asset for researchers across various domains.