Estimation of Linear and Nonlinear Errors-in-Variables Models Using Validation Data
作者:
Lung-Fei Lee,
JungsywanH. Sepanski,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1995)
卷期:
Volume 90,
issue 429
页码: 130-140
ISSN:0162-1459
年代: 1995
DOI:10.1080/01621459.1995.10476495
出版商: Taylor & Francis Group
关键词: Bias;Consistent estimator;Efficiency;Measurement error model;Monte Carlo;Nonlinear least squares;Primary data;Projection;Wide sense expectation
数据来源: Taylor
摘要:
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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