Empirical Likelihood Method for Censored Median Regression Models

2009 ◽  
Vol 38 (8) ◽  
pp. 1170-1183 ◽  
Author(s):  
Wen Yu ◽  
Yunting Sun ◽  
Ming Zheng
Keyword(s):  
Empirical Likelihood ◽  
Regression Models ◽  
Likelihood Method ◽  
Median Regression ◽  
Empirical Likelihood Method

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.

Empirical Likelihood Based Testing for Polynomial Regression Models

2014 ◽  
Vol 599-601 ◽  
pp. 927-930
Author(s):  
Pei Xin Zhao
Keyword(s):  
Data Analysis ◽  
Empirical Likelihood ◽  
Regression Models ◽  
Polynomial Regression ◽  
Real Data ◽  
Testing Procedure ◽  
Likelihood Method ◽  
Empirical Likelihood Method ◽  
Testing Method ◽  
Polynomial Regression Models

Based on the empirical likelihood method, a testing procedure is proposed for polynomial regression models. Some simulations and a real data analysis are undertaken to investigate the power of the empirical likelihood based testing method.

scholarly journals Empirical Likelihood for Generalized Functional-Coefficient Regression Models with Multiple Smoothing Variables under Right Censoring Data

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Hong-Xia Xu ◽  
Han-Sheng Zhong ◽  
Guo-Liang Fan
Keyword(s):  
Empirical Likelihood ◽  
Unknown Parameter ◽  
Regression Models ◽  
Confidence Region ◽  
Right Censoring ◽  
Likelihood Method ◽  
True Parameter ◽  
Empirical Likelihood Method ◽  
Nonparametric Approach ◽  
Functional Coefficient

Empirical likelihood as a nonparametric approach has been demonstrated to have many desirable merits for constructing a confidence region. The purpose of this article is to apply the empirical likelihood method to study the generalized functional-coefficient regression models with multiple smoothing variables when the response is subject to random right censoring. The coefficient functions with multiple smoothing variables can accommodate various nonlinear interaction effects between covariates. The empirical log-likelihood ratio of an unknown parameter is constructed and shown to have a standard chi-squared limiting distribution at the true parameter. Based on this, the confidence region of the unknown parameter can be constructed. Simulation studies are carried out to indicate that the empirical likelihood method performs better than a normal approximation-based approach for constructing the confidence region.

Empirical Likelihood Testing for Longitudinal Varying Coefficient Models

2014 ◽  
Vol 518 ◽  
pp. 356-360
Author(s):  
Chang Qing Liu
Keyword(s):  
Data Analysis ◽  
Empirical Likelihood ◽  
Real Data ◽  
Likelihood Method ◽  
Varying Coefficient Models ◽  
Empirical Likelihood Method ◽  
Real Data Analysis ◽  
Varying Coefficient ◽  
Testing Method

By using the empirical likelihood method, a testing method is proposed for longitudinal varying coefficient models. Some simulations and a real data analysis are undertaken to investigate the power of the empirical likelihood based testing method.

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