Sepanski, J. and Lee, L.F. (1995) Estimation of Linear and Nonlinear Errors-in-Variables Models Using Validation Data. Journal of the American Statistical Association, 90, 130-140.
https://doi.org/10.1080/01621459.1995.10476495

has been cited by the following article:

• TITLE: Estimation of Nonparametric Regression Models with Measurement Error Using Validation Data

  AUTHORS: Fang Liu, Zanhua Yin

  KEYWORDS: Ill-Posed Inverse Problems, Measurement Errors, Nonparametric Regression, Orthogonal Series

  JOURNAL NAME: Applied Mathematics, Vol.8 No.10, October 31, 2017

  ABSTRACT: 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.