Semiparametric generalized varying coefficient partially linear models with longitudinal data arise in contemporary biology, medicine, and life science. In this paper, we consider a variable selection procedure based on the combination of the basis function approximations and quadratic inference functions with SCAD penalty. The proposed procedure simultaneously selects significant variables in the parametric components and the nonparametric components. With appropriate selection of the tuning parameters, we establish the consistency, sparsity, and asymptotic normality of the resulting estimators. The finite sample performance of the proposed methods is evaluated through extensive simulation studies and a real data analysis.

中文翻译：

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纵向数据的广义局部线性变系数模型基于惩罚二次方函数的变量选择

具有纵向数据的半参数广义可变系数部分线性模型出现在当代生物学，医学和生命科学中。在本文中，我们考虑基于基函数逼近和二次推理函数与SCAD惩罚相结合的变量选择过程。所提出的过程同时选择了参数分量和非参数分量中的重要变量。随着调整参数的适当选择，我们建立一致性，稀疏，所得估计的渐近正态性。通过大量的模拟研究和真实的数据分析来评估所提出方法的有限样本性能。