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

Xinran Zhang, Xiaohui Yuan, Chunjie Wang and Xinyuan Song

Computational Statistics & Data Analysis, 2025, vol. 211, issue C

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.

Keywords: Censored data; Cure fraction; Monotone quantile regression neural network; Data augmentation; Non-crossing (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:211:y:2025:i:c:s0167947325000775

DOI: 10.1016/j.csda.2025.108201

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