scholarly journals Penalized Empirical Likelihood based Variable Selection for Partially Linear Quantile Regression Models with Missing Responses

2016 ◽  
Vol 47 (141) ◽  
pp. 1-1
Author(s):  
Xinrong Tang ◽  
Peixin Zhao
Keyword(s):  
Variable Selection ◽  
Quantile Regression ◽  
Empirical Likelihood ◽  
Regression Models ◽  
Missing Responses ◽  
Partially Linear ◽  
Selection For ◽  
Penalized Empirical Likelihood

scholarly journals Quantile regression and variable selection for partially linear model with randomly truncated data

2017 ◽  
Vol 60 (4) ◽  
pp. 1137-1160 ◽  
Author(s):  
Hong-Xia Xu ◽  
Zhen-Long Chen ◽  
Jiang-Feng Wang ◽  
Guo-Liang Fan
Keyword(s):  
Variable Selection ◽  
Quantile Regression ◽  
Linear Model ◽  
Partially Linear Model ◽  
Truncated Data ◽  
Partially Linear ◽  
Selection For

Penalized Estimation Based Variable Selection for Semiparametric Regression Models with Endogenous Covariates

2014 ◽  
Vol 1079-1080 ◽  
pp. 843-846
Author(s):  
Pei Xin Zhao
Keyword(s):  
Variable Selection ◽  
Empirical Likelihood ◽  
Regression Models ◽  
Selection Procedure ◽  
Semiparametric Regression ◽  
Variable Selection Procedure ◽  
Endogenous Variables ◽  
Endogenous Covariates ◽  
Variable Selection Problem ◽  
Penalized Empirical Likelihood

In this paper, we study the variable selection problem for the parametric components of semiparametric regression models with endogenous variables. Based on the penalized empirical likelihood technology and the bias adjustment method, we propose a penalized empirical likelihood based variable selection procedure. Simulation studies show that the proposed variable selection procedure is workable, and the resulting estimator is consistent.

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