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

Kernel-based random effect time-varying coefficient model for longitudinal data

2017 ◽  
Vol 267 ◽  
pp. 500-507 ◽  
Author(s):  
Jooyong Shim ◽  
Insuk Sohn ◽  
Changha Hwang
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.

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