Empirical Likelihood of Quantile Difference with Missing Response When High-dimensional Covariates Are Present

2021 ◽  
Vol 37 (12) ◽  
pp. 1803-1825
Author(s):  
Cui Juan Kong ◽  
Han Ying Liang
Keyword(s):  
Empirical Likelihood ◽  
High Dimensional ◽  
Missing Response

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.

Empirical likelihood for high-dimensional linear regression models

Metrika ◽  
2013 ◽  
Vol 77 (7) ◽  
pp. 921-945 ◽  
Author(s):  
Hong Guo ◽  
Changliang Zou ◽  
Zhaojun Wang ◽  
Bin Chen
Keyword(s):  
Linear Regression ◽  
Empirical Likelihood ◽  
Regression Models ◽  
High Dimensional ◽  
Linear Regression Models
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