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Monotone composite quantile regression neural network for censored data with a cure fraction

Author

Listed:
  • Zhang, Xinran
  • Yuan, Xiaohui
  • Wang, Chunjie
  • Song, Xinyuan

Abstract

The cure rate monotone composite quantile regression neural network model is investigated as an extension of the cure rate quantile model. It can uncover complex nonlinear relationships and effectively ensure the non-crossing of quantile predictions. An iterative algorithm coupled with data augmentation is developed to predict the survival time of susceptible subjects and the cure rate among all subjects. Simulation studies indicate that the proposed approach exhibits advantages in prediction over traditional statistical methods in finite samples when nonlinearity exists between response and predictors. The analysis of two real datasets further validates the utility of the proposed method.

Suggested Citation

  • Zhang, Xinran & Yuan, Xiaohui & Wang, Chunjie & Song, Xinyuan, 2025. "Monotone composite quantile regression neural network for censored data with a cure fraction," Computational Statistics & Data Analysis, Elsevier, vol. 211(C).
    • Handle: RePEc:eee:csdana:v:211:y:2025:i:c:s0167947325000775
    DOI: 10.1016/j.csda.2025.108201
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