Expectile Regression With Errors-in-Variables

This paper studies the expectile regression with error-in-variables to reduce the data error and describe the overall data distribution. Specifically, the asymptotic normality of the proposed estimator is thoroughly investigated, and an IRWLS algorithm based on orthogonal distance expectile regression (ODER) is proposed to estimate the parameters. Extensive simulation studies and real data applications evaluate our method's capabilities in reducing the measurement error bias, demonstrating our model's parameter estimation effectiveness, and its capability in reducing the simulation error compared with linear and quantile regression schemes.

Automated summary

This paper investigates the expectile regression with error-in-variables to reduce data error and describe the overall data distribution. It thoroughly examines the asymptotic normality of the proposed estimator and proposes an IRWLS algorithm based on orthogonal distance expectile regression (ODER) to estimate the parameters. Extensive simulation studies and real data applications evaluate the effectiveness of our method in reducing measurement error bias, and demonstrate its capability in reducing simulation error compared to linear and quantile regression schemes.