Akaike's information criterion in generalized estimating equations

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Abstract

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.

Original languageEnglish (US)
Pages (from-to)120-125
Number of pages6
JournalBiometrics
Volume57
Issue number1
DOIs
StatePublished - 2001

Bibliographical note

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

Keywords

  • Akaike Information Criterion
  • Generalized estimating equations
  • Generalized linear models
  • Model selection
  • Quasi-likelihood

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