Robust Double-Adaptive Regularized Weight Expectile Method and Its Application in GDP Data
In order to solve the problem of penalty expectile regression failure when leverage points exist,based on expectile regression and robust double adaptive penalty weight regression estimation method,this paper proposes a robust double adaptive penalty weight expectile regression estimation method.This method can realize robust variable selection and heteroscedasticity detection when both response variables and covariates contain outliers.For the proposed model,this paper first uses MM algorithm to construct the optimal control function instead of the penalty function,and then uses the iterative weighted least squares estimation algorithm to estimate the parameters.The penalty parameters are ob-tainned by minimizing the BIC criterion.Simulation and empirical results show that the proposed method outperforms the penalized least squares method and the penalized quantile regression method in terms of variable selection and heteroscedasticity detection when there are leverage points in the data.
万方数据
维普
