Simulation-Extrapolation Estimation in Parametric Measurement Error Models
作者:
J.R. Cook,
L.A. Stefanski,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1994)
卷期:
Volume 89,
issue 428
页码: 1314-1328
ISSN:0162-1459
年代: 1994
DOI:10.1080/01621459.1994.10476871
出版商: Taylor & Francis Group
关键词: Correction for attenuation;Extrapolation;Framingham Heart Study;Logistic regression;Measurement error model;Method of moments;Nonlinear model;Simulation
数据来源: Taylor
摘要:
We describe a simulation-based method of inference for parametric measurement error models in which the measurement error variance is known or at least well estimated. The method entails adding additional measurement error in known increments to the data, computing estimates from the contaminated data, establishing a trend between these estimates and the variance of the added errors, and extrapolating this trend back to the case of no measurement error. We show that the method is equivalent or asymptotically equivalent to method-of-moments estimation in linear measurement error modeling. Simulation studies are presented showing that the method produces estimators that are nearly asymptotically unbiased and efficient in standard and nonstandard logistic regression models. An oversimplified but fairly accurate description of the method is that it is method-of-moments estimation using Monte Carlo-derived estimating equations.
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