Estimation of Nonparametric Regression Models with Measurement Error Using Validation Data  

作　　者：Fang Liu Zanhua Yin 

机构地区：[1]College of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China

出　　处：《Applied Mathematics》2017年第10期1454-1463,共10页应用数学（英文）

摘　　要：We consider the problem of estimating a function g in nonparametric regression model when only some of covariates are measured with errors with the assistance of validation data. Without specifying any error model structure between the surrogate and true covariables, we propose an estimator which integrates orthogonal series estimation and truncated series approximation method. Under general regularity conditions, we get the convergence rate of this estimator. Simulations demonstrate the finite-sample properties of the new estimator.We consider the problem of estimating a function g in nonparametric regression model when only some of covariates are measured with errors with the assistance of validation data. Without specifying any error model structure between the surrogate and true covariables, we propose an estimator which integrates orthogonal series estimation and truncated series approximation method. Under general regularity conditions, we get the convergence rate of this estimator. Simulations demonstrate the finite-sample properties of the new estimator.

关 键 词：ILL-POSED INVERSE PROBLEMS Measurement ERRORS NONPARAMETRIC Regression ORTHOGONAL Series 

分 类 号：O1[理学—数学]