You are not currently logged in.
Access JSTOR through your library or other institution:
Simulation-Extrapolation Estimation in Parametric Measurement Error Models
J. R. Cook and L. A. Stefanski
Journal of the American Statistical Association
Vol. 89, No. 428 (Dec., 1994), pp. 1314-1328
Stable URL: http://www.jstor.org/stable/2290994
Page Count: 15
You can always find the topics here!Topics: Estimators, Simulations, Statistical estimation, Modeling, Estimation methods, Mathematical extrapolation, Datasets, Regression analysis, Linear regression, Statistical variance
Were these topics helpful?See something inaccurate? Let us know!
Select the topics that are inaccurate.
Preview not available
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 oversimiplified but fairly accurate description of the method is that it is method-of-moments estimation using Monte Carlo-derived estimating equations.
Journal of the American Statistical Association © 1994 American Statistical Association