数学杂志2021，Vol.41Issue(3)：189-204,16.
基于当前状态数据的加法风险模型的自适应LASSO回归选择
REGRESSION SELECTION VIA THE ADAPTIVE LASSO FOR CURRENT STATUS DATA UNDER THE ADDITIVE HAZARDS MODEL
摘要
Abstract
Variable selection is commonly employed when the true underlying model has a sparse representation.Identifying significant predictors will enhance the prediction performance of the fitted model.To solve this problem,among others,Zhang and Lu[1]developed a variable selection method under the framework of the proportional hazards model when one observes right-censored data.In this paper,We consider the variable selection problem for the additive hazards model when one faces current status data.Motivated by Zhang and Lu[1],we develop an adaptive Lasso method for this problem.Some theoretical properties,including consistency and oracle properties are established under some weak regularity conditions.An extensive simulation is performed to show that the method performs competitively.This method is also applied to a real data setfrom a tumorigenicity study.关键词
加法风险模型/当前状态数据/自适应Lasso/ADMM算法Key words
Additive hazards model/current status data/adaptive Lasso/ADMM algorithm分类
数理科学引用本文复制引用
张怿瑾,王成勇..基于当前状态数据的加法风险模型的自适应LASSO回归选择[J].数学杂志,2021,41(3):189-204,16.基金项目
Supported by National Natural Science Foundation of China(71371066). （71371066）
