CENSORED DATA MODELING: A NOVEL ANTI-REGRESSION FRAMEWORK

Censored data, where the exact value of an observation is not fully observed, poses a challenge in statistical modelling. Traditional regression approaches often fail to adequately handle such leading biased estimates and inaccurate predictions. In this study, we propose novel anti-regression framework specifically designed for censored data The integrates advanced techniques incorporates mechanisms mitigate impact censoring. By leveraging information available from observations, our approach provides more reliable improved predictive performance compared traditional methods. We validate effectiveness through extensive simulations real-world case studies. results demonstrate superiority proposed accurately modelling highlighting its potential various applications fields as medical research, finance, engineering. This study contributes advancement valuable tool researchers practitioners dealing with their analyses.