EFFICIENT ESTIMATION OF FUNCTIONAL-COEFFICIENT REGRESSION MODELS WITH DIFFERENT SMOOTHING VARIABLES  

作　　者：张日权 李国英 

机构地区：[1]Department of Mathematics,Shanxi Datong University [2]Department of Statistics,East China Normal University [3]Academy of Mathematics and System Sciences,Chinese Academy of Sciences

出　　处：《Acta Mathematica Scientia》2008年第4期989-997,共9页数学物理学报（B辑英文版）

基　　金：the Shanxi NaturalScience Foundation(2007011014)

摘　　要：In this article,a procedure for estimating the coefficient functions on the functional-coefficient regression models with different smoothing variables in different coefficient functions is defined.First step,by the local linear technique and the averaged method,the initial estimates of the coefficient functions are given.Second step,based on the initial estimates,the efficient estimates of the coefficient functions are proposed by a one-step back-fitting procedure.The efficient estimators share the same asymptotic normalities as the local linear estimators for the functional-coefficient models with a single smoothing variable in different functions.Two simulated examples show that the procedure is effective.In this article,a procedure for estimating the coefficient functions on the functional-coefficient regression models with different smoothing variables in different coefficient functions is defined.First step,by the local linear technique and the averaged method,the initial estimates of the coefficient functions are given.Second step,based on the initial estimates,the efficient estimates of the coefficient functions are proposed by a one-step back-fitting procedure.The efficient estimators share the same asymptotic normalities as the local linear estimators for the functional-coefficient models with a single smoothing variable in different functions.Two simulated examples show that the procedure is effective.

关 键 词：Asymptotic normality averaged method different smoothing variables functional-coefficient regression models local linear method one-step back-fitting procedure 

分 类 号：O212.1[理学—概率论与数理统计]