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Akaike's Information Criterion in Generalized Estimating Equations

Akaike's Information Criterion in Generalized Estimating Equations 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Wiley

Akaike's Information Criterion in Generalized Estimating Equations

Biometrics , Volume 57 (1) – Mar 1, 2001

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References (30)

Publisher
Wiley
Copyright
Copyright © 2001 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0006-341X
eISSN
1541-0420
DOI
10.1111/j.0006-341X.2001.00120.x
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

BiometricsWiley

Published: Mar 1, 2001

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