Regularized Buckley–James Method: A Comprehensive Review and Applications

Abstract

The Regularized Buckley–James (RBJ) method has emerged as a powerful tool in the field of survival analysis, offering robust solutions for handling censored data and covariates with complex structures. This paper provides a comprehensive review of the RBJ method, highlighting its theoretical foundations, algorithmic implementations, and practical applications. We delve into the regularization techniques employed in the RBJ method, emphasizing their role in enhancing model performance and interpretability. Through simulated experiments and real-world case studies, we showcase the efficacy of the RBJ method in various domains, including medical research, engineering, and social sciences. Additionally, we discuss recent advancements, challenges, and future directions in the utilization of the RBJ method, paving the way for further innovations in survival analysis and related fields.