Smooth-threshold GEE variable selection for varying coefficient partially linear models with longitudinal data

2015 ◽  
Vol 44 (3) ◽  
pp. 419-431 ◽  
Author(s):  
Ruiqin Tian ◽  
Liugen Xue ◽  
Yuping Hu
Keyword(s):  
Variable Selection ◽  
Longitudinal Data ◽  
Linear Models ◽  
Partially Linear Models ◽  
Varying Coefficient ◽  
Partially Linear ◽  
Selection For

scholarly journals Variable selection for partially linear models via learning gradients

2017 ◽  
Vol 11 (2) ◽  
pp. 2907-2930 ◽  
Author(s):  
Lei Yang ◽  
Yixin Fang ◽  
Junhui Wang ◽  
Yongzhao Shao
Keyword(s):  
Variable Selection ◽  
Linear Models ◽  
Partially Linear Models ◽  
Partially Linear ◽  
Selection For

scholarly journals Penalized Quadratic Inference Function-Based Variable Selection for Generalized Partially Linear Varying Coefficient Models with Longitudinal Data

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jinghua Zhang ◽  
Liugen Xue
Keyword(s):  
Variable Selection ◽  
Longitudinal Data ◽  
Linear Models ◽  
Selection Procedure ◽  
Real Data ◽  
Finite Sample ◽  
Varying Coefficient ◽  
Partially Linear ◽  
Quadratic Inference Function ◽  
Quadratic Inference Functions

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.

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