Generalized empirical likelihood inference in partially linear model for longitudinal data with missing response variables and error-prone covariates

2016 ◽  
Vol 46 (19) ◽  
pp. 9743-9762
Author(s):  
Juanfang Liu ◽  
Liugen Xue ◽  
Ruiqin Tian
Keyword(s):  
Longitudinal Data ◽  
Linear Model ◽  
Empirical Likelihood ◽  
Likelihood Inference ◽  
Partially Linear Model ◽  
Missing Response ◽  
Generalized Empirical Likelihood ◽  
Partially Linear ◽  
Response Variables

Empirical Likelihood Inference for a Partially Linear Model under Longitudinal Data

2013 ◽  
Vol 353-356 ◽  
pp. 3355-3358
Author(s):  
Yu Ying Jiang
Keyword(s):  
Longitudinal Data ◽  
Linear Model ◽  
Empirical Likelihood ◽  
Confidence Regions ◽  
Repeated Measurements ◽  
Correlation Structure ◽  
Regression Coefficients ◽  
Partially Linear Model ◽  
Chi Square ◽  
Partially Linear

In this paper, a partially linear model under longitudinal data is considered. In order to take into consideration the within-subject correlation structure of the repeated measurements, an empirical likelihood incorporating the correlation structure is developed. The asymptotic normality of the maximum empirical likelihood estimates of the regression coefficients is obtained. It also can be shown that the proposed empirical likelihood ratio is asymptotically standard chi-square. The results can be used directly to construct the asymptotic confidence regions of the regression coefficients. The convergence rate of the baseline function is derived.

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