Akaike's Information Criterion in Generalized Estimating Equations

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ID: 290486
2001
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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.
Reference Key
openalex_W2110776215 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wei Pan
Journal biometrics
Year 2001
DOI
10.1111/j.0006-341x.2001.00120.x
URL
https://doi.org/10.1111/j.0006-341x.2001.00120.x
Keywords Keywords not found

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