Sample Empirical Likelihood and the Design-based Oracle Variable Selection Theory

2022 ◽  
Author(s):  
Puying Zhao ◽  
David Haziza ◽  
Changbao Wu
Keyword(s):  
Variable Selection ◽  
Empirical Likelihood ◽  
Selection Theory

Bayesian empirical likelihood and variable selection for censored linear model with applications to acute myelogenous leukemia data

2019 ◽  
Vol 12 (05) ◽  
pp. 1950050
Author(s):  
Chun-Jing Li ◽  
Hong-Mei Zhao ◽  
Xiao-Gang Dong
Keyword(s):  
Variable Selection ◽  
Empirical Likelihood ◽  
Likelihood Function ◽  
Myelogenous Leukemia ◽  
Correct Identification ◽  
Analysis Tool ◽  
Linear Regression Models ◽  
Identification Rate ◽  
Selection For ◽  
Leukemia Data

This paper develops the Bayesian empirical likelihood (BEL) method and the BEL variable selection for linear regression models with censored data. Empirical likelihood is a multivariate analysis tool that has been widely applied to many fields such as biomedical and social sciences. By introducing two special priors to the empirical likelihood function, we find two obvious superiorities of the BEL methods, that is (i) more precise coverage probabilities of the BEL credible region and (ii) higher accuracy and correct identification rate of the BEL model selection using an hierarchical Bayesian model, vs. some current methods such as the LASSO, ALASSO and SCAD. The numerical simulations and empirical analysis of two data examples show strong competitiveness of the proposed method.

Empirical likelihood based variable selection

2010 ◽  
Vol 140 (4) ◽  
pp. 971-981 ◽  
Author(s):  
Asokan Mulayath Variyath ◽  
Jiahua Chen ◽  
Bovas Abraham
Keyword(s):  
Variable Selection ◽  
Empirical Likelihood

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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