ESTIMATION OF LINEAR AND NONLINEAR ERRORS-IN-VARIABLES MODELS USING VALIDATION DATA
ESTIMATION OF LINEAR AND NONLINEAR ERRORS-IN-VARIABLES MODELS USING VALIDATION DATA
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DOI：
10.2307/2291136
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发表时间：
1995-03-01
影响因子：
3.7
通讯作者：
SEPANSKI, JH
中科院分区：
文献类型：
--
作者：
LEE, LF;SEPANSKI, JH
提出了在存在验证数据的情况下具有变量测量误差的线性和非线性回归模型的一致估计器。估计过程基于最小二乘法，用广义条件期望函数代替回归函数。这些方法不依赖于分布假设，并且对于测量误差模型的错误指定具有鲁棒性。它们在计算和分析上比基于非参数回归或密度函数的半参数方法更简单。
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.