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

Pei Xin Zhao

doi:10.4028/www.scientific.net/AMR.1079-1080.843

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1079-1080Penalized Estimation Based Variable Selection for...

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

Abstract:

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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Periodical:

Advanced Materials Research (Volumes 1079-1080)

Pages:

843-846

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1079-1080.843

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Online since:

December 2014

Authors:

Pei Xin Zhao*

Keywords:

Bias Adjustment, Penalized Estimation Method, Semiparametric Regression Model, Variable Selection

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