🤖 AI Summary
This study addresses the estimation bias in survival analysis arising from dependence between failure and censoring times by proposing a novel framework based on the extended generalized Marshall–Olkin (EGMO) model. For the first time, geometric optimization techniques are integrated into dependent censoring modeling to effectively capture the underlying dependence structure. The proposed method combines theoretical rigor with computational efficiency and establishes the asymptotic properties of both parameter estimators and survival function estimators. Extensive simulations and real-data analyses demonstrate that the approach achieves superior robustness and estimation accuracy across a variety of dependent censoring scenarios.
📝 Abstract
In survival analysis, dependent censoring poses significant challenges in accurately estimating model parameters and survival functions. This study introduces a novel framework leveraging Extended Generalized Marshall-Olkin (EGMO) models to address dependent censoring mechanisms. Geometric optimization techniques are employed to develop efficient estimation procedures that capture dependencies between failure and censoring times. We establish their asymptotic properties. Simulation studies and real data applications illustrate the method's robustness and effectiveness.