Efficient Quantile Estimation for Functional-Coefficient Partially Linear Regression Models  

作　　者：Zhangong ZHOU Rong JIANG Weimin QIAN 

机构地区：[1]Department of Statistics, Jiaxing University, Jiaxing 314001,zhejiang, China [2]Department of Mathematics, Tongji University, Shanghai 200092, China [3]Corresponding author. Department of Mathematics, Tongji University, Shanghai 200092, China

出　　处：《Chinese Annals of Mathematics,Series B》2011年第5期729-740,共12页数学年刊（B辑英文版）

基　　金：supported by the Zhejiang Provincial Natural Science Foundation of China (No. Y6110662)

摘　　要：The quantile estimation methods are proposed for functional-coefficient partially linear regression （FCPLR） model by combining nonparametric and functional-coefficient regression （FCR） model. The local linear scheme and the integrated method are used to obtain Focal quantile estimators of all unknown functions in the FCPLR model. These resulting estimators are asymptotically normal, but each of them has big variance. To reduce variances of these quantile estimators, the one-step backfitting technique is used to obtain the efficient quantile estimators of all unknown functions, and their asymptotic normalities are derived. Two simulated examples are carried out to illustrate the proposed estimation methodology.

关 键 词：Functional-coefficient model Quantile regression Local linear method Backfitting technique Asymptotic normality 

分 类 号：O212.7[理学—概率论与数理统计] TN911.7[理学—数学]