Journal article
The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements
Wiley interdisciplinary reviews. Computational statistics, Vol.11(3), p.n/a
05/2019
DOI: 10.1002/wics.1460
Abstract
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 estimation with structural and dimensional determination. This article reviews the conceptual and theoretical foundations for AIC, discusses its properties and its predictive interpretation, and provides a synopsis of important practical issues pertinent to its application. Comparisons and delineations are drawn between AIC and its primary competitor, the Bayesian information criterion (BIC). In addition, the article covers refinements of AIC for settings where the asymptotic conditions and model specification assumptions that underlie the justification of AIC may be violated.
This article is categorized under:
Software for Computational Statistics > Artificial Intelligence and Expert Systems
Statistical Models > Model Selection
Statistical and Graphical Methods of Data Analysis > Modeling Methods and Algorithms
Statistical and Graphical Methods of Data Analysis > Information Theoretic Methods
Hirotugu Akaike (November 5, 1927 to August 4, 2009)
Details
- Title: Subtitle
- The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements
- Creators
- Joseph E Cavanaugh - University of Iowa, Iowa CityAndrew A Neath - Southern Illinois University
- Resource Type
- Journal article
- Publication Details
- Wiley interdisciplinary reviews. Computational statistics, Vol.11(3), p.n/a
- DOI
- 10.1002/wics.1460
- ISSN
- 1939-5108
- eISSN
- 1939-0068
- Publisher
- John Wiley & Sons, Inc
- Number of pages
- 11
- Language
- English
- Date published
- 05/2019
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Injury Prevention Research Center
- Record Identifier
- 9984214674402771
Metrics
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