Model Selection in Estimating Equations
Article first published online: 21 MAY 2004
Volume 57, Issue 2, pages 529–534, June 2001
How to Cite
Pan, W. (2001), Model Selection in Estimating Equations. Biometrics, 57: 529–534. doi: 10.1111/j.0006-341X.2001.00529.x
- Issue published online: 21 MAY 2004
- Article first published online: 21 MAY 2004
- Received December 1998. Revised September 2000. Accepted October 2000.
- Akaike information criterion;
- Bayesian information criterion;
- Generalized estimating equations;
- Generalized linear models;
Summary. Model selection is a necessary step in many practical regression analyses. But for methods based on estimating equations, such as the quasi-likelihood and generalized estimating equation (GEE) approaches, there seem to be few well-studied model selection techniques. In this article, we propose a new model selection criterion that minimizes the expected predictive bias (EPB) of estimating equations. A bootstrap smoothed cross-validation (BCV) estimate of EPB is presented and its performance is assessed via simulation for overdispersed generalized linear models. For illustration, the method is applied to a real data set taken from a study of the development of ewe embryos.