Empirical likelihood for linear regression models with missing responses

2009 ◽  
Vol 79 (11) ◽  
pp. 1391-1396 ◽  
Author(s):  
Yongsong Qin ◽  
Ling Li ◽  
Qingzhu Lei
Keyword(s):  
Linear Regression ◽  
Empirical Likelihood ◽  
Regression Models ◽  
Linear Regression Models ◽  
Missing Responses

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

scholarly journals Empirical Likelihood Inference for Partial Functional Linear Regression Models Based on B-spline

2018 ◽  
Vol 8 (1) ◽  
pp. 135
Author(s):  
Mingao Yuan ◽  
Yue Zhang
Keyword(s):  
Linear Regression ◽  
Empirical Likelihood ◽  
Regression Models ◽  
Confidence Regions ◽  
Likelihood Method ◽  
Linear Regression Models ◽  
B Spline ◽  
Empirical Likelihood Method ◽  
Regression Parameters ◽  
Functional Linear Regression

In this paper, we apply empirical likelihood method to infer for the regression parameters in the partial functional linear regression models based on B-spline. We prove that the empirical log-likelihood ratio for the regression parameters converges in law to a weighted sum of independent chi-square distributions. Our simulation shows that the proposed empirical likelihood method produces more accurate confidence regions in terms of coverage probability than the asymptotic normality method.

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