Orthogonal Series Estimation of Nonparametric Regression Measurement Error Models with Validation Data  

作　　者：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12期1820-1831,共12页应用数学（英文）

摘　　要：In this article we study the estimation method of nonparametric regression measurement error model based on a validation data. The estimation procedures are based on orthogonal series estimation and truncated series approximation methods without specifying any structure equation and the distribution assumption. The convergence rates of the proposed estimator are derived. By example and through simulation, the method is robust against the misspecification of a measurement error model.In this article we study the estimation method of nonparametric regression measurement error model based on a validation data. The estimation procedures are based on orthogonal series estimation and truncated series approximation methods without specifying any structure equation and the distribution assumption. The convergence rates of the proposed estimator are derived. By example and through simulation, the method is robust against the misspecification of a measurement error model.

关 键 词：ILL-POSED INVERSE Problems Measurement ERRORS NONPARAMETRIC Regression ORTHOGONAL Series VALIDATION Data 

分 类 号：O1[理学—数学]