Simulation-Extrapolation: The Measurement Error Jackknife

This article provides theoretical support for our simulation-based estimation procedure, SIMEX, for measurement error models. We do so by establishing a strong relationship between SIMEX estimation and jackknife estimation. A result of our investigation is the identification of a variance estimation...

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Bibliographic Details
Published in:Journal of the American Statistical Association 1995-12, Vol.90 (432), p.1247-1256
Main Authors: Stefanski, L. A., Cook, J. R.
Format: Article
Language:English
Subjects:
Bias reduction
Components of variance
Contour lines
Estimation bias
Estimation methods
Estimators
Estimators for the mean
Exact sciences and technology
Extrapolation
Linear inference, regression
Linear models
Linear regression
Logistic regression
Mathematical extrapolation
Mathematical models
Mathematics
Maximum likelihood estimation
Measurement
Nonparametric inference
Probability and statistics
Sciences and techniques of general use
Simulation
Standard error
Statistical analysis
Statistical methods
Statistical variance
Statistics
Theory and Methods
Unbiased estimators
Uniform minimum variance unbiased estimation
Variance estimation
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Summary:This article provides theoretical support for our simulation-based estimation procedure, SIMEX, for measurement error models. We do so by establishing a strong relationship between SIMEX estimation and jackknife estimation. A result of our investigation is the identification of a variance estimation method for SIMEX that parallels jackknife variance estimation. Data from the Framingham Heart Study are used to illustrate the variance estimation procedure in logistic regression measurement error models.
ISSN:0162-1459
1537-274X
DOI:10.1080/01621459.1995.10476629