Efficient Inference about the Partially Linear Varying Coefficient Model with Random Effect for Longitudinal Data

2013 ◽  
pp. 573-583
Author(s):  
Wanbin Li ◽  
Liugen Xue
Keyword(s):  
Longitudinal Data ◽  
Random Effect ◽  
Varying Coefficient Model ◽  
Varying Coefficient ◽  
Partially Linear

scholarly journals Functional random effect time-varying coefficient model for longitudinal data

Stat ◽  
2012 ◽  
Vol 1 (1) ◽  
pp. 75-89 ◽  
Author(s):  
Jeng-Min Chiou ◽  
Yanyuan Ma ◽  
Chih-Ling Tsai
Keyword(s):  
Longitudinal Data ◽  
Random Effect ◽  
Time Varying ◽  
Varying Coefficient Model ◽  
Varying Coefficient ◽  
Effect Time

scholarly journals Block Empirical Likelihood for Longitudinal Single-Index Varying-Coefficient Model

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Yunquan Song ◽  
Ling Jian ◽  
Lu Lin
Keyword(s):  
Longitudinal Data ◽  
Empirical Likelihood ◽  
Coverage Probability ◽  
Confidence Region ◽  
Asymptotic Covariance Matrix ◽  
Varying Coefficient Model ◽  
Varying Coefficient ◽  
Single Index ◽  
Asymptotic Covariance ◽  
Normal Approximations

In this paper, we consider a single-index varying-coefficient model with application to longitudinal data. In order to accommodate the within-group correlation, we apply the block empirical likelihood procedure to longitudinal single-index varying-coefficient model, and prove a nonparametric version of Wilks’ theorem which can be used to construct the block empirical likelihood confidence region with asymptotically correct coverage probability for the parametric component. In comparison with normal approximations, the proposed method does not require a consistent estimator for the asymptotic covariance matrix, making it easier to conduct inference for the model's parametric component. Simulations demonstrate how the proposed method works.

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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