Akaike's Information Criterion in Generalized Estimating Equations
Article first published online: 24 MAY 2004
Volume 57, Issue 1, pages 120–125, March 2001
How to Cite
Pan, W. (2001), Akaike's Information Criterion in Generalized Estimating Equations. Biometrics, 57: 120–125. doi: 10.1111/j.0006-341X.2001.00120.x
- Issue published online: 24 MAY 2004
- Article first published online: 24 MAY 2004
- Received June 1999. Revised December 1999 and June 2000. Accepted June 2000.
- Akaike Information Criterion;
- Generalized estimating equations;
- Generalized linear models;
- Model selection;
Summary. Correlated response data are common in biomedical studies. Regression analysis based on the generalized estimating equations (GEE) is an increasingly important method for such data. However, there seem to be few model-selection criteria available in GEE. The well-known Akaike Information Criterion (AIC) cannot be directly applied since AIC is based on maximum likelihood estimation while GEE is nonlikelihood based. We propose a modification to AIC, where the likelihood is replaced by the quasi-likelihood and a proper adjustment is made for the penalty term. Its performance is investigated through simulation studies. For illustration, the method is applied to a real data set.