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 data, leading to biased estimates and inaccurate predictions. In this study, we propose a novel anti-regression framework specifically designed for censored data modelling. The framework integrates advanced statistical techniques and incorporates mechanisms to mitigate the impact of censoring. By leveraging the information available from censored observations, our approach provides more reliable estimates and improved predictive performance compared to traditional regression methods. We validate the effectiveness of our framework through extensive simulations and real-world case studies. The results demonstrate the superiority of the proposed anti-regression framework in accurately modelling censored data, highlighting its potential for various applications in fields such as medical research, finance, and engineering. This study contributes to the advancement of statistical modelling techniques for censored data and provides a valuable tool for researchers and practitioners dealing with such data in their analyses.
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