Author:
Abo El Nasr Mona Mahmoud,Abdelmegaly Alaa A.,Abdo Doaa A.
Abstract
AbstractThis paper provides a comprehensive analysis of linear regression models, focusing on addressing multicollinearity challenges in breast cancer patient data. Linear regression methodologies, including GAM, Beta, GAM Beta, Ridge, and Beta Ridge, are compared using two statistical criteria. The study, conducted with R software, showcases the Beta regression model’s exceptional performance, achieving a BIC of − 5520.416. Furthermore, the Ridge regression model demonstrates remarkable results with the best AIC at − 8002.647. The findings underscore the practical application of these models in real-world scenarios and emphasize the Beta regression model’s superior ability to handle multicollinearity challenges. The preference for AIC over BIC in Generalized Additive Models (GAMs) is rooted in the AIC’s calculation framework, highlighting its effectiveness in capturing the complexity and flexibility inherent in GAMs.
Publisher
Springer Science and Business Media LLC
Reference44 articles.
1. Akram, M. N., Amin, M., Elhassanein, A. & Ullah, M. A. A new modified ridge-type estimator for the beta regression model: Simulation and application. AIMS Math. 7, 10351057 (2022).
2. Anderson, C. J., Verkuilen, J. & Johnson, T. Applied generalized linear mixed models: Continuous and discrete data. Soc. Behav. Sci. 63, 89 (2010).
3. Geissinger, E. A., Khoo, C. L., Richmond, I. C., Faulkner, S. J. & Schneider, D. C. A case for beta regression in the natural sciences. Ecosphere 13, e3940 (2022).
4. Ferrari, S. & Cribari-Neto, F. Beta regression for modelling rates and proportions. J. Appl. Stat. 31, 799–815 (2004).
5. Qasim, M., Maansson, K. & Golam Kibria, B. On some beta ridge regression estimators: Method, simulation and application. J. Stat. Comput. Simul. 91, 1699–1712 (2021).