测量误差模型的自适应LASSO变量选择方法研究

Abstract

This paper focuses on variable selection and parameter estimation for measurement error models via adaptive LASSO method. Firstly, the adaptive LASSO estimator for linear models and partially linear models are proposed when the covariates are measured with error. Under some regular conditions the asymptotic properties of the estimators are investigated, it is proved that the adaptive lasso estimator has the oracle properties with proper choices of tuning parameter. Moreover, the algorithms and choices of penalty parameter and bandwidth are discussed. Finally, a Monte Carlo simulation study and a real data analysis are conducted to assess the finite sample performance of the proposed variable selection procedure. The results show that the adaptive LASSO estimator behaves well.