Department of Information Engineering, Jingdezhen Ceramic Institute, Jiangxi, China.
College of Applied Sciences, Beijing University of Technology, Beijing, China.
Comput Math Methods Med. 2020 Oct 5;2020:3505306. doi: 10.1155/2020/3505306. eCollection 2020.
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.
具有纵向数据的半参数广义变系数部分线性模型在当代生物学、医学和生命科学中出现。本文考虑了一种基于基函数逼近和二次推断函数与 SCAD 惩罚相结合的变量选择方法。所提出的方法同时选择参数组件和非参数组件中的显著变量。通过适当选择调整参数，我们建立了所得估计量的一致性、稀疏性和渐近正态性。通过广泛的模拟研究和实际数据分析评估了所提出方法的有限样本性能。
