An efficient estimation for the parameter in additive partially linear models with missing covariates

Abstract

In this paper, we study the weighted quantile average estimation technique for the parameter in additive partially linear models with missing covariates, which is proved to be an efficient method. The proposed method is based on optimally combining information over different quantiles via multiple quantile regression. We establish asymptotic normality of the weighted quantile average estimators when the selection probability is known, estimated using the non-parametrical method and parametrical method, respectively. Moreover, we compute optimal weights by minimizing asymptotic variance and then obtain the corresponding optimal weighted quantile average estimators. To examine the finite performance of our proposed method, we use the numerical simulations and apply to model time sober for the patients from a rehabilitation center. Simulation results and data analysis further verify that the proposed method is an efficient and safe alternative to both the WCQR method and WLS method.