For the high-dimensional data, the number of covariates can be large and diverge with the sample size. In this work, we propose an adaptive bi-level penalized method to solve the group variable selection problem for the recurrent event model with a diverging number of covariates. Comparing with the classical group variable selection methods, the adaptive bi-level penalized method can select the important group variables and individual variables simultaneously. For the case of diverging a number of covariates, we demonstrate that the proposed method has selection consistency and the penalized estimators have asymptotic normality. Simulation studies show that the proposed method performs well and the results are consistent with the theoretical properties. The proposed method is illustrated by analyzing a real life data set.

中文翻译：

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具有不同协变量数量的复发事件模型的组变量选择

对于高维数据，协变量的数量可能很大并且随着样本大小而发散。在这项工作中，我们提出了一种自适应双级惩罚方法来解决具有不同协变量数量的循环事件模型的组变量选择问题。与经典的组变量选择方法相比，自适应双水平惩罚方法可以同时选择重要的组变量和个体变量。对于发散多个协变量的情况，我们证明了所提出的方法具有选择一致性，并且惩罚估计量具有渐近正态性。仿真研究表明，该方法性能良好，结果与理论性质一致。通过分析现实生活中的数据集来说明所提出的方法。