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Akaike's Information Criterion in Generalized Estimating Equations
Vol. 57, No. 1 (Mar., 2001), pp. 120-125
Published by: International Biometric Society
Stable URL: http://www.jstor.org/stable/2676849
Page Count: 6
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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 quasilikelihood 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.
Biometrics © 2001 International Biometric Society