标题：An adaptive estimation for covariate-adjusted nonparametric regression model
作者：Li F.; Lin L.; Lu Y.; Feng S.
作者机构：[Li, F] School of Mathematics and Statistics, Zhengzhou University, Zhengzhou, China;[ Lin, L] Zhongtai Securities Institute for Financial Studies, Sh 更多
通讯作者地址：[Lin, L] Zhongtai Securities Institute for Financial Studies, Shandong UniversityChina;
关键词：Adaptability; Asymmetric distribution; Covariate-adjusted regression; Efficiency; Nonparametric estimation
摘要：For covariate-adjusted nonparametric regression model, an adaptive estimation method is proposed for estimating the nonparametric regression function. Compared with the procedures introduced in the existing literatures, the new method needs less strict conditions and is adaptive to covariate-adjusted nonparametric regression with asymmetric variables. More specifically, when the distributions of the variables are asymmetric, the new procedures can gain more efficient estimators and recover data more accurately by elaborately choosing proper weights; and for the symmetric case, the new estimators can obtain the same asymptotic properties as those obtained by the existing method via designing equal bandwidths and weights. Simulation studies are carried out to examine the performance of the new method in finite sample situations and the Boston Housing data is analyzed as an illustration. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.