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

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…