Monotone composite quantile regression neural network for censored data with a cure fraction
研究了一种单调复合分位数回归神经网络模型，用于分析存在治愈分数的删失数据，能处理复杂非线性关系并避免分位数预测交叉，通过模拟和真实数据验证了其预测优势。
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.