Variable Selection with Incomplete Covariate Data
Article first published online: 27 MAR 2008
© 2008, The International Biometric Society
Volume 64, Issue 4, pages 1062–1069, December 2008
How to Cite
Claeskens, G. and Consentino, F. (2008), Variable Selection with Incomplete Covariate Data. Biometrics, 64: 1062–1069. doi: 10.1111/j.1541-0420.2008.01003.x
- Issue published online: 24 NOV 2008
- Article first published online: 27 MAR 2008
- Received March 2007. Revised November 2007. Accepted December 2007.
- Akaike information criterion;
- EM algorithm;
- Missing covariates;
- Model selection;
- Takeuchi's information criterion
Summary Application of classical model selection methods such as Akaike's information criterion (AIC) becomes problematic when observations are missing. In this article we propose some variations on the AIC, which are applicable to missing covariate problems. The method is directly based on the expectation maximization (EM) algorithm and is readily available for EM-based estimation methods, without much additional computational efforts. The missing data AIC criteria are formally derived and shown to work in a simulation study and by application to data on diabetic retinopathy.