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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1079-1080.843 ◽

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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A Stepwise Variable Selection Procedure for Nonlinear Regression Models

Biometrics ◽

10.2307/2530219 ◽

1980 ◽

Vol 36 (3) ◽

pp. 511 ◽

Cited By ~ 54

Author(s):

P. N. Peduzzi ◽

R. J. Hardy ◽

T. R. Holford

Keyword(s):

Variable Selection ◽

Nonlinear Regression ◽

Regression Models ◽

Selection Procedure ◽

Nonlinear Regression Models ◽

Stepwise Variable Selection ◽

Variable Selection Procedure

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

Hacettepe Journal of Mathematics and Statistics ◽

10.15672/hjms.201614120416 ◽

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

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A pseudo knockoff filter for correlated features

Information and Inference A Journal of the IMA ◽

10.1093/imaiai/iay012 ◽

2018 ◽

Vol 8 (2) ◽

pp. 313-341

Author(s):

Jiajie Chen ◽

Anthony Hou ◽

Thomas Y Hou

Keyword(s):

Variable Selection ◽

False Discovery Rate ◽

Numerical Experiments ◽

Selection Procedure ◽

Numerical Examples ◽

False Discovery ◽

Variable Selection Procedure ◽

Partial Analysis ◽

False Discovery Proportion

Abstract In Barber & Candès (2015, Ann. Statist., 43, 2055–2085), the authors introduced a new variable selection procedure called the knockoff filter to control the false discovery rate (FDR) and proved that this method achieves exact FDR control. Inspired by the work by Barber & Candès (2015, Ann. Statist., 43, 2055–2085), we propose a pseudo knockoff filter that inherits some advantages of the original knockoff filter and has more flexibility in constructing its knockoff matrix. Moreover, we perform a number of numerical experiments that seem to suggest that the pseudo knockoff filter with the half Lasso statistic has FDR control and offers more power than the original knockoff filter with the Lasso Path or the half Lasso statistic for the numerical examples that we consider in this paper. Although we cannot establish rigourous FDR control for the pseudo knockoff filter, we provide some partial analysis of the pseudo knockoff filter with the half Lasso statistic and establish a uniform false discovery proportion bound and an expectation inequality.

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