Akaike Information Criterion (AIC)

When a model involving q parameters is fitted to data, the criterion is defined as -2Lq + 2q where Lq is the maximized log likelihood. Akaike suggested maximizing the criterion to choose between models with different numbers of parameters. It was originally proposed for time-series models, but is also used in regression. In STATISTICA, Akaike Information Criterion (AIC) can be used in the Generalized Linear/Nonlinear Models (GLZ) module when comparing the subsets of effects during best subset regression; note that the computation of the AIC statistic can require some time. Since the evaluation of the score statistic does not require iterative computations, best subset selection based on the score statistic is computationally faster, while the selection based on the AIC statistic usually provides more accurate results. See GLZ Quick Specs Dialog - Advanced tab for further details.

In Structural Equation Modeling, AIC can be computed using the discrepancy function with the formula Fk + 2v/(N+1) where Fk is the discrepancy function of the model with k parameters, v is the degrees of freedom for the model, and N is the sample size.