scholarly journals Correction to “Empirical‐likelihood‐based criteria for model selection on marginal analysis of longitudinal data with dropout missingness,” by Chen, C., Shen, B., Zhang, L., Xue, Y. and Wang, M.; 75(3), 950–965, 2019

Biometrics ◽  
2021 ◽  
Vol 77 (2) ◽  
pp. 779-779
Keyword(s):  
Model Selection ◽  
Longitudinal Data ◽  
Empirical Likelihood ◽  
Marginal Analysis

scholarly journals Empirical‐likelihood‐based criteria for model selection on marginal analysis of longitudinal data with dropout missingness

Biometrics ◽  
2019 ◽  
Vol 75 (3) ◽  
pp. 950-965
Author(s):  
Chixiang Chen ◽  
Biyi Shen ◽  
Lijun Zhang ◽  
Yuan Xue ◽  
Ming Wang
Keyword(s):  
Model Selection ◽  
Longitudinal Data ◽  
Empirical Likelihood ◽  
Marginal Analysis

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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