Estimation of Linear and Nonlinear Errors-in-Variables Models Using Validation Data
提出利用验证数据估计线性与非线性变量含误差模型的一致估计量，基于最小二乘法和广义条件期望，不依赖分布假设，对测量误差模型误设稳健，计算简单。
Abstract Consistent estimators for linear and nonlinear regression models with measurement errors in variables in the presence of validation data are proposed. The estimation procedures are based on least squares methods with regression functions replaced by wide-sense conditional expectation functions. The methods do not depend on distributional assumptions and are robust against the misspecification of a measurement error model. They are computationally and analytically simpler than semiparametric methods based on nonparametric regression or density functions. Key Words: BiasConsistent estimatorEfficiencyMeasurement error modelMonte CarloNonlinear least squaresPrimary dataProjectionWide sense expectation