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.

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.

Empirical Likelihood Testing for Linear Instrumental Variable Regression Models

2014 ◽  
Vol 1008-1009 ◽  
pp. 1501-1504
Author(s):  
Pei Xin Zhao
Keyword(s):  
Linear Regression ◽  
Empirical Likelihood ◽  
Instrumental Variables ◽  
Instrumental Variable ◽  
Regression Models ◽  
Testing Procedure ◽  
Likelihood Method ◽  
Linear Regression Models ◽  
Testing Method ◽  
Instrumental Variable Regression

Based on the empirical likelihood method, an instrumental variable based testing procedure is proposed for linear regression models with instrumental variables. The proposed testing method can attenuate the effect of endogeneity of covariates. Some simulations indicate that the proposed testing method is more powerful.

Testing Based on Empirical Likelihood for Partially Linear Models with Instrumental Variables

2014 ◽  
Vol 624 ◽  
pp. 500-504
Author(s):  
Pei Xin Zhao
Keyword(s):  
Empirical Likelihood ◽  
Instrumental Variables ◽  
Instrumental Variable ◽  
Linear Models ◽  
Testing Procedure ◽  
Likelihood Method ◽  
Partially Linear Models ◽  
Testing Method ◽  
Partially Linear ◽  
Instrumental Variable Method

This paper considers the model testing for partially linear models with instrumental variables. By combining the instrumental variable method and the empirical likelihood method, an instrumental variable type testing procedure is proposed. The proposed testing procedure can attenuate the effect of endogeneity of covariates. Some simulations imply that the instrumental variable based empirical likelihood testing method is more poweful.

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 Interval Estimation of Random Coefficient Integer-Valued Autoregressive Model Based on Mean Empirical Likelihood Method

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xianghong Xu ◽  
Dehui Wang ◽  
Zhiwen Zhao
Keyword(s):  
Data Analysis ◽  
Empirical Likelihood ◽  
Autoregressive Model ◽  
Interval Estimation ◽  
Real Data ◽  
Confidence Regions ◽  
Random Coefficient ◽  
Likelihood Method ◽  
First Order ◽  
The Mean

In this paper, we study the use of the mean empirical likelihood (MEL) method in a first-order random coefficient integer-valued autoregressive model. The MEL ratio statistic is established, its limiting properties are discussed, and the confidence regions for the parameter of interest are derived. Furthermore, a simulation study is presented to demonstrate the performance of the proposed method. Finally, a real data analysis of dengue fever is performed.

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.

Sign in / Sign up

Export Citation Format

Share Document