Robust empirical likelihood estimation for variable partially linear models via weighted composite quantile regression
The robust empirical likelihood inferences for varying coefficient partially linear models are studied.Using weighted composite quantile regression and empirical likelihood method,combined with orthogonal projection technology based on matrix QR decomposition,an empirical likelihood estimation method based on weighted composite fractional regression is proposed for parameter components of the model.Theoretical proof has been provided that the proposed empirical logarithmic likelihood ratio function asymptotically follows a chi square distribution,thereby constructing confidence intervals for parameter components.The orthogonal projection technique based on matrix QR decomposition is introduced in this estimation method,which ensures that the estimation of parameter components is not affected by the estimation accuracy of nonparametric components,so it has better robustness and effectiveness.
NETL
NSTL
万方数据
维普
