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Akaike’s Information Criterion: Background, Derivation, Properties, and Refinements

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International Encyclopedia of Statistical Science

Introduction

The Akaike Information Criterion, AIC, was introduced by Hirotogu Akaike in his seminal 1973 paper “Information Theory and an Extension of the Maximum Likelihood Principle.” AIC was the first model selection criterion to gain widespread attention in the statistical community. Today, AIC continues to be the most widely known and used model selection tool among practitioners.

The traditional maximum likelihood paradigm, as applied to statistical modeling, provides a mechanism for estimating the unknown parameters of a model having a specified dimension and structure. Akaike extended this paradigm by considering a framework in which the model dimension is also unknown, and must therefore be determined from the data. Thus, Akaike proposed a framework wherein both model estimation and selection could be simultaneously accomplished.

For a parametric candidate model of interest, the likelihood function reflects the conformity of the model to the observed data. As the complexity...

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References and Further Readings

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Author information

Authors and Affiliations

  1. The University of Iowa, Iowa City, IA, USA

    Joseph E. Cavanaugh (Professor) & Andrew A. Neath (Professor)

Authors
  1. Joseph E. Cavanaugh
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  2. Andrew A. Neath
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Editor information

Editors and Affiliations

  1. Department of Statistics and Informatics, Faculty of Economics, University of Kragujevac, City of Kragujevac, Serbia

    Miodrag Lovric

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© 2011 Springer-Verlag Berlin Heidelberg

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Cavanaugh, J.E., Neath, A.A. (2011). Akaike’s Information Criterion: Background, Derivation, Properties, and Refinements. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_111

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