# 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.