Empirical likelihood inference in mixture of semiparametric varying-coefficient models for longitudinal data with non-ignorable dropout

Statistics ◽  
2012 ◽  
Vol 48 (3) ◽  
pp. 668-684 ◽  
Author(s):  
Xing-Cai Zhou ◽  
Jin-Guan Lin
Keyword(s):  
Longitudinal Data ◽  
Empirical Likelihood ◽  
Likelihood Inference ◽  
Varying Coefficient Models ◽  
Varying Coefficient

scholarly journals Block Empirical Likelihood for Semiparametric Varying-Coefficient Partially Linear Errors-in-Variables Models with Longitudinal Data

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Yafeng Xia ◽  
Hu Da
Keyword(s):  
Longitudinal Data ◽  
Likelihood Ratio ◽  
Empirical Likelihood ◽  
Likelihood Ratio Statistic ◽  
Likelihood Inference ◽  
Errors In Variables ◽  
Varying Coefficient ◽  
Mild Conditions ◽  
Partially Linear ◽  
Log Likelihood

Block empirical likelihood inference for semiparametric varying-coeffcient partially linear errors-in-variables models with longitudinal data is investigated. We apply the block empirical likelihood procedure to accommodate the within-group correlation of the longitudinal data. The block empirical log-likelihood ratio statistic for the parametric component is suggested. And the nonparametric version of Wilk’s theorem is derived under mild conditions. Simulations are carried out to access the performance of the proposed procedure.

Empirical Likelihood Testing for Longitudinal Varying Coefficient Models

2014 ◽  
Vol 518 ◽  
pp. 356-360
Author(s):  
Chang Qing Liu
Keyword(s):  
Data Analysis ◽  
Empirical Likelihood ◽  
Real Data ◽  
Likelihood Method ◽  
Varying Coefficient Models ◽  
Empirical Likelihood Method ◽  
Real Data Analysis ◽  
Varying Coefficient ◽  
Testing Method

By using the empirical likelihood method, a testing method is proposed for longitudinal varying coefficient models. Some simulations and a real data analysis are undertaken to investigate the power of the empirical likelihood based testing method.

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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