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A note on conditional aic for linear mixed-effects models

  • Hua Liang
  • Hulin Wu
  • Guohua Zou

The conventional model selection criterion, the Akaike information criterion, aic , has been applied to choose candidate models in mixed-effects models by the consideration of marginal likelihood. Vaida & Blanchard (2005) demonstrated that such a marginal aic and its small sample correction are inappropriate when the research focus is on clusters. Correspondingly, these authors suggested the use of conditional aic . Their conditional aic is derived under the assumption that the variance-covariance matrix or scaled variance-covariance matrix of random effects is known. This note provides a general conditional aic but without these strong assumptions. Simulation studies show that the proposed method is promising. Copyright 2008, Oxford University Press.

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Article provided by Biometrika Trust in its journal Biometrika.

Volume (Year): 95 (2008)
Issue (Month): 3 ()
Pages: 773-778

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Handle: RePEc:oup:biomet:v:95:y:2008:i:3:p:773-778
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