Empirical likelihood inference in linear regression with nonignorable missing response

2014 ◽  
Vol 79 ◽  
pp. 91-112 ◽  
Author(s):  
Cuizhen Niu ◽  
Xu Guo ◽  
Wangli Xu ◽  
Lixing Zhu
Keyword(s):  
Linear Regression ◽  
Empirical Likelihood ◽  
Likelihood Inference ◽  
Missing Response ◽  
Nonignorable Missing

Model checking for general linear regression with nonignorable missing response

2019 ◽  
Vol 138 ◽  
pp. 1-12 ◽  
Author(s):  
Xu Guo ◽  
Lianlian Song ◽  
Yun Fang ◽  
Lixing Zhu
Keyword(s):  
Linear Regression ◽  
Model Checking ◽  
General Linear ◽  
Missing Response ◽  
Nonignorable Missing

scholarly journals Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hanji He ◽  
Guangming Deng
Keyword(s):  
Empirical Likelihood ◽  
Missing At Random ◽  
Confidence Regions ◽  
Likelihood Inference ◽  
High Dimensional ◽  
Finite Sample ◽  
The Mean ◽  
Data Missing ◽  
Consistency And Asymptotic Normality ◽  
The Impact

We extend the mean empirical likelihood inference for response mean with data missing at random. The empirical likelihood ratio confidence regions are poor when the response is missing at random, especially when the covariate is high-dimensional and the sample size is small. Hence, we develop three bias-corrected mean empirical likelihood approaches to obtain efficient inference for response mean. As to three bias-corrected estimating equations, we get a new set by producing a pairwise-mean dataset. The method can increase the size of the sample for estimation and reduce the impact of the dimensional curse. Consistency and asymptotic normality of the maximum mean empirical likelihood estimators are established. The finite sample performance of the proposed estimators is presented through simulation, and an application to the Boston Housing dataset is shown.

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