scholarly journals Changepoint Analysis by Modified Empirical Likelihood Method in Two-phase Linear Regression Models

2013 ◽  
Vol 03 (01) ◽  
pp. 1-6 ◽  
Author(s):  
Hualing Zhao ◽  
Hanfeng Chen ◽  
Wei Ning
Keyword(s):  
Linear Regression ◽  
Empirical Likelihood ◽  
Regression Models ◽  
Likelihood Method ◽  
Linear Regression Models ◽  
Two Phase ◽  
Empirical Likelihood Method ◽  
Changepoint Analysis

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 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.

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 Penalized Maximum Likelihood Method to a Class of Skewness Data Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Xuedong Chen ◽  
Qianying Zeng ◽  
Qiankun Song
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Expectation Maximization Algorithm ◽  
Simulated Data ◽  
Likelihood Method ◽  
Skew Normal Distribution ◽  
Linear Regression Models ◽  
Heavy Tailed Distributions ◽  
Random Term ◽  
Heavy Tailed

An extension of some standard likelihood and variable selection criteria based on procedures of linear regression models under the skew-normal distribution or the skew-tdistribution is developed. This novel class of models provides a useful generalization of symmetrical linear regression models, since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions. A generalized expectation-maximization algorithm is developed for computing thel1penalized estimator. Efficacy of the proposed methodology and algorithm is demonstrated by simulated data.

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