A Perturbation Subsampling Method for Massive Censored Data

With the advancement of information technology, large-scale data have become increasingly common. Subsampling methods for the statistical analysis of such data require computing the sampling probability for each observation, a process that can be computationally intensive. In this paper, we extend the perturbed subsampling approach to the Cox proportional hazards model, a widely used method in survival analysis to address the statistical analysis of large-scale survival data. Specifically, we propose a perturbed subsampling algorithm for this model. The effectiveness of the proposed method is evaluated through simulation studies and real-data analysis.