纵向数据与生存数据的联合模型—基于机器学习方法
The Joint Model of Longitudinal and Survival Data—Based on Machine Learning Methods

作者: 温征 :云南师范大学数学学院,云南 昆明;

关键词: 联合模型机器学习殃残差Cox-Snell残差Joint Model Machine Learning Martingale Residuals Cox-Snell Residuals

摘要: 本文运用机器学习方法对纵向数据与生存数据建模,以机器学习方法代替纵向子模型中的线性随机效应模型;生存子模型仍运用Cox比例危险模型。与传统的建模方法做对比,此建模方法的生存子模型残差图诊断符合理论结果,纵向子模型的残差要比线性混合模型分散。

Abstract: In this paper, machine learning methods for longitudinal data and survival data modeling, replace the longitudinal sub-model linear random effects model; survival sub-model still uses Cox propor-tional hazards model. Compared with the traditional method, the residuals plots of survival sub- model diagnose modeling methods in line with theoretical results and the residuals of the longi-tudinal sub models are more dispersed than the linear mixed model.

文章引用: 温征 (2015) 纵向数据与生存数据的联合模型—基于机器学习方法。 统计学与应用, 4, 252-261. doi: 10.12677/SA.2015.44028

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