Empirical likelihood for response differences in two linear regression models with missing data

2015 ◽  
Vol 31 (4) ◽  
pp. 963-976
Author(s):  
Yong-song Qin ◽  
Tao Qiu ◽  
Qing-zhu Lei
Keyword(s):  
Missing Data ◽  
Linear Regression ◽  
Empirical Likelihood ◽  
Regression Models ◽  
Linear Regression Models

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.

scholarly journals Comparison study on the confidence intervals in linear regression models with restricted parameter space

2019 ◽  
Vol 52 (2) ◽  
pp. 115-127
Author(s):  
XIUQIN BAI ◽  
WEIXING SONG
Keyword(s):  
Linear Regression ◽  
Empirical Likelihood ◽  
Regression Models ◽  
Confidence Region ◽  
Estimation Procedure ◽  
Regression Coefficients ◽  
Comparison Study ◽  
Linear Regression Models ◽  
Finite Sample ◽  
Confidence Estimation

This paper proposes an empirical likelihood confidence region for the regression coefficients in linear regression models when the regression coefficients are subjected to some equality constraints. The shape of the confidence set does not depend on the re-parametrization of the regression model induced by the equality constraint. It is shown that the asymptotic coverage rate attains the nominal confidence level and the Bartlett correction can successfully reduce the coverage error rate from $O(n^{-1})$ to $O(n^{-2})$, where n denotes the sample size. Simulation studies are conducted to evaluate the finite sample performance of the proposed empirical likelihood empirical confidence estimation procedure. Finally, a comparison study is conducted to compare the finite sample performance of the proposed and the classical ellipsoidal confidence sets based on normal theory.

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