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Multimodel Inference

Author

Listed:
  • Kenneth P. Burnham
  • David R. Anderson

    (Colorado Cooperative Fish and Wildlife Research Unit (USGS-BRD))

Abstract

The model selection literature has been generally poor at reflecting the deep foundations of the Akaike information criterion (AIC) and at making appropriate comparisons to the Bayesian information criterion (BIC). There is a clear philosophy, a sound criterion based in information theory, and a rigorous statistical foundation for AIC. AIC can be justified as Bayesian using a “savvy†prior on models that is a function of sample size and the number of model parameters. Furthermore, BIC can be derived as a non-Bayesian result. Therefore, arguments about using AIC versus BIC for model selection cannot be from a Bayes versus frequentist perspective. The philosophical context of what is assumed about reality, approximating models, and the intent of model-based inference should determine whether AIC or BIC is used. Various facets of such multimodel inference are presented here, particularly methods of model averaging.

Suggested Citation

  • Kenneth P. Burnham & David R. Anderson, 2004. "Multimodel Inference," Sociological Methods & Research, , vol. 33(2), pages 261-304, November.
    • Handle: RePEc:sae:somere:v:33:y:2004:i:2:p:261-304
    DOI: 10.1177/0049124104268644
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    References listed on IDEAS

    as
    1. Akaike, Hirotugu, 1981. "Likelihood of a model and information criteria," Journal of Econometrics, Elsevier, vol. 16(1), pages 3-14, May.
    2. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    2. Qian Chen & David E. Giles & Hui Feng, 2012. "The extreme-value dependence between the Chinese and other international stock markets," Applied Financial Economics, Taylor & Francis Journals, vol. 22(14), pages 1147-1160, July.
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