A note on conditional aic for linear mixed-effects models
AbstractThe 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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Bibliographic InfoArticle provided by Biometrika Trust in its journal Biometrika.
Volume (Year): 95 (2008)
Issue (Month): 3 ()
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- Yu, Dalei & Yau, Kelvin K.W., 2012. "Conditional Akaike information criterion for generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 629-644.
- Braun, Julia & Sabanés Bové, Daniel & Held, Leonhard, 2014. "Choice of generalized linear mixed models using predictive crossvalidation," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 190-202.
- Yu, Dalei & Zhang, Xinyu & Yau, Kelvin K.W., 2013. "Information based model selection criteria for generalized linear mixed models with unknown variance component parameters," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 245-262.
- Dimova, Rositsa B. & Markatou, Marianthi & Talal, Andrew H., 2011. "Information methods for model selection in linear mixed effects models with application to HCV data," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2677-2697, September.
- Kubokawa, Tatsuya, 2011. "Conditional and unconditional methods for selecting variables in linear mixed models," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 641-660, March.
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