Model pursuit and variable selection in the additive accelerated failure time model
Model pursuit and variable selection in the additive accelerated failure time model
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DOI：
10.1007/s00362-020-01205-0
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发表时间：
2020-10
影响因子：
1.3
通讯作者：
Li Liu;Hao Wang;Yanyan Liu;Jian Huang
中科院分区：
文献类型：
--
作者：
Li Liu;Hao Wang;Yanyan Liu;Jian Huang
在本文中，我们提出了一种新的半参数方法，可以在加性 AFT 模型中同时选择重要变量、识别模型结构并估计协变量效应，其中协变量的维数允许随着样本大小的增加而增加。我们没有像大多数现有研究那样直接近似非参数效应，而是采取线性效应来弱化模型可识别性所需的条件。为了以数值方式计算所提出的估计，我们使用乘法器算法的交替方向方法，以便它可以轻松实现并实现快速收敛速度。我们的方法被证明是选择一致的并且具有渐近预言性质。通过模拟和真实数据分析说明了所提出方法的性能。
In this paper, we propose a new semiparametric method to simultaneously select important variables, identify the model structure and estimate covariate effects in the additive AFT model, for which the dimension of covariates is allowed to increase with sample size. Instead of directly approximating the non-parametric effects as in most existing studies, we take a linear effect out to weak the condition required for model identifiability. To compute the proposed estimates numerically, we use an alternating direction method of multipliers algorithm so that it can be implemented easily and achieve fast convergence rate. Our method is proved to be selection consistent and possess an asymptotic oracle property. The performance of the proposed methods is illustrated through simulations and the real data analysis.