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基于当前状态数据的加法风险模型的自适应LASSO回归选择
张怿瑾,王成勇
作者单位
张怿瑾 武汉大学数学与统计学院, 湖北 武汉 430072 
王成勇 湖北文理学院数学与统计学院, 湖北 襄阳 441053 
摘要:
当真实的潜在模型具有稀疏表示时通常需要使用变量选择方法,确定模型中的重要预测因子可提高被拟合模型的预测性能,许多文献研究了这类问题,其中张和吕[1]针对右删失数据开发了一种基于比例风险模型的变量选择方法.本文研究了基于当前状态数据的加法风险模型的变量选择问题.在文献[1]的启发下,我们提出一种自适应Lasso方法来解决这个问题,并在弱正则性条件下,建立了估计量的相合性和oracle性质等理论结果.大量的模拟数据分析证明了该方法的有效性.我们用该方法分析了一组来自肿瘤研究的真实数据.
关键词:  加法风险模型  当前状态数据  自适应Lasso  ADMM算法
DOI:
分类号:O212.1
基金项目:Supported by National Natural Science Foundation of China(71371066).
REGRESSION SELECTION VIA THE ADAPTIVE LASSO FOR CURRENT STATUS DATA UNDER THE ADDITIVE HAZARDS MODEL
ZHANG Yi-jin,WANG Cheng-yong
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 set from a tumorigenicity study.
Key words:  Additive hazards model  current status data  adaptive Lasso  ADMM algorithm

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