The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements

@article{Cavanaugh2019TheAI,
  title={The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements},
  author={Joseph E. Cavanaugh and Andrew A. Neath},
  journal={Wiley Interdisciplinary Reviews: Computational Statistics},
  year={2019},
  volume={11}
}
  • J. Cavanaugh, A. Neath
  • Published 14 March 2019
  • Computer Science
  • Wiley Interdisciplinary Reviews: Computational Statistics
The Akaike information criterion (AIC) is one of the most ubiquitous tools in statistical modeling. The first model selection criterion to gain widespread acceptance, AIC was introduced in 1973 by Hirotugu Akaike as an extension to the maximum likelihood principle. Maximum likelihood is conventionally applied to estimate the parameters of a model once the structure and dimension of the model have been formulated. Akaike's seminal idea was to combine into a single procedure the process of… 

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