Composite Quantile Regression for Measurement Error Models with Mixed Random Missing Data
In this paper,we study composite quantile regression(CQR)and variable selection of linear errors-in-variables models where the response and multi-dimensional covariates are mixed random missing.In order to improve the estimation efficiency,we propose the CQR estimator of regression coefficients based on inverse probability weighting and measurement error correction factor.The proposed CQR estimator can not only eliminate the influence of measurement errors on estimation results,but also deal with mixed random missing data effectively.At the same time,the asymptotic normality of the proposed estimator is obtained.Furthermore,a variable selection method based on the adaptive LASSO penalty is investigated for the measurement error models with mixed random missing data.The oracle property of the proposed penalized estimator is also established.Meanwhile,Monte Carlo simulation studies and a real data analysis are conducted to demonstrate the finite sample performance of the proposed methods.
Composite quantile regressionmeasurement errormixed random miss-ingvariable selectionasymptotic normality
万方数据
维普
