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A comparison of the variance estimation methods for heteroscedastic nonlinear models
Kurex Sidik1, Jeffrey N Jonkman2
1Bristol-Myers Squibb Company, Princeton, NJ, U.S.A.
Statistics in Medicine
|July 8, 2016
Summary
This study compares eight variance estimation methods for nonlinear regression with heterogeneous variances. The mean variance function and transform-both-sides methods are best when variance depends on the mean, while bootstrap and sandwich estimators offer good general performance.
Area of Science:
- Statistics
- Econometrics
- Biostatistics
Background:
- Heteroscedasticity is a common issue in nonlinear regression.
- Accurate variance estimation is crucial for reliable parameter inference.
Purpose of the Study:
- To evaluate eight variance estimation methods for nonlinear regression with heterogeneous variances.
- To compare their performance in estimating standard errors of model parameters.
Main Methods:
- Simulation study comparing eight distinct variance estimation techniques.
- Analysis focused on accuracy and bias of standard error estimates.
Main Results:
- Mean variance function and transform-both-sides methods excel when variance is a function of the mean.
- Wild bootstrap and modified sandwich estimators show reasonable accuracy and low bias, particularly for nonsystematic heterogeneity.
Conclusions:
- Modified sandwich estimators are practical due to computational advantages.
- The choice of method depends on the nature of variance heterogeneity in nonlinear models.
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