Empirical Likelihood for Partially Linear Models with Missing Responses: The Fixed Design Case

2011 ◽  
Vol 40 (10) ◽  
pp. 1849-1865 ◽  
Author(s):  
Yongsong Qin ◽  
Yinghua Li
Keyword(s):  
Empirical Likelihood ◽  
Linear Models ◽  
Partially Linear Models ◽  
Missing Responses ◽  
Partially Linear ◽  
Fixed Design ◽  
Design Case

scholarly journals Empirical Likelihood-Based Inferences for Generalized Partially Linear Models

2009 ◽  
Vol 36 (3) ◽  
pp. 433-443 ◽  
Author(s):  
HUA LIANG ◽  
YONGSONG QIN ◽  
XINYU ZHANG ◽  
DAVID RUPPERT
Keyword(s):  
Empirical Likelihood ◽  
Linear Models ◽  
Partially Linear Models ◽  
Partially Linear

Testing Based on Empirical Likelihood for Partially Linear Models with Instrumental Variables

2014 ◽  
Vol 624 ◽  
pp. 500-504
Author(s):  
Pei Xin Zhao
Keyword(s):  
Empirical Likelihood ◽  
Instrumental Variables ◽  
Instrumental Variable ◽  
Linear Models ◽  
Testing Procedure ◽  
Likelihood Method ◽  
Partially Linear Models ◽  
Testing Method ◽  
Partially Linear ◽  
Instrumental Variable Method

This paper considers the model testing for partially linear models with instrumental variables. By combining the instrumental variable method and the empirical likelihood method, an instrumental variable type testing procedure is proposed. The proposed testing procedure can attenuate the effect of endogeneity of covariates. Some simulations imply that the instrumental variable based empirical likelihood testing method is more poweful.

Quantile Regression for Partially Linear Models with Missing Responses at Random

2015 ◽  
Vol 727-728 ◽  
pp. 1013-1016
Author(s):  
Pei Xin Zhao
Keyword(s):  
Quantile Regression ◽  
Linear Models ◽  
Estimation Method ◽  
Regression Method ◽  
Partially Linear Models ◽  
Missing Response ◽  
Missing Responses ◽  
Partially Linear ◽  
Simulation Results ◽  
Quantile Regression Method

In this paper, we propose a weighted quantile regression method for partially linear models with missing response at random. The proposed estimation method can give an efficient estimator for parametric components, and can attenuate the effect of missing responses. Some simulations are carried out to assess the performance of the proposed estimation method, and simulation results indicate that the proposed method is workable.

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