Journal
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
Volume 86, Issue 10, Pages 1891-1900Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00949655.2015.1089873
Keywords
correlation selection; Bias correction; efficiency; generalized estimating equations; empirical covariance matrix; 62J12; 62F99
Funding
- National Center for Research Resources
- National Center for Advancing Translational Sciences, National Institutes of Health [UL1TR000117]
Ask authors/readers for more resources
When generalized estimating equations (GEEs) incorporate an unstructured working correlation matrix, the variances of regression parameter estimates can inflate due to the estimation of the correlation parameters. In previous work, an approximation for this inflation that results in a corrected version of the sandwich formula for the covariance matrix of regression parameter estimates was derived. Use of this correction for correlation structure selection also reduces the over-selection of the unstructured working correlation matrix. In this manuscript, we conduct a simulation study to demonstrate that an increase in variances of regression parameter estimates can occur when GEE incorporates structured working correlation matrices as well. Correspondingly, we show the ability of the corrected version of the sandwich formula to improve the validity of inference and correlation structure selection. We also study the relative influences of two popular corrections to a different source of bias in the empirical sandwich covariance estimator.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available