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Estimation of Linear and Nonlinear Errors-in-Variables Models Using Validation Data

Lung-fei Lee and Jungsywan H. Sepanski
Journal of the American Statistical Association
Vol. 90, No. 429 (Mar., 1995), pp. 130-140
DOI: 10.2307/2291136
Stable URL: http://www.jstor.org/stable/2291136
Page Count: 11
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Estimation of Linear and Nonlinear Errors-in-Variables Models Using Validation Data
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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.

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