协变量
估计员
比例危险模型
维数之咒
计算机科学
应用数学
线性模型
计量经济学
数学
统计
人工智能
机器学习
作者
Qiang Wu,Xingwei Tong,Xingqiu Zhao
标识
DOI:10.1093/biomtc/ujae024
摘要
Abstract Deep learning has continuously attained huge success in diverse fields, while its application to survival data analysis remains limited and deserves further exploration. For the analysis of current status data, a deep partially linear Cox model is proposed to circumvent the curse of dimensionality. Modeling flexibility is attained by using deep neural networks (DNNs) to accommodate nonlinear covariate effects and monotone splines to approximate the baseline cumulative hazard function. We establish the convergence rate of the proposed maximum likelihood estimators. Moreover, we derive that the finite-dimensional estimator for treatment covariate effects is $\sqrt{n}$-consistent, asymptotically normal, and attains semiparametric efficiency. Finally, we demonstrate the performance of our procedures through extensive simulation studies and application to real-world data on news popularity.
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