What is Akaike's information criterion

AIC (Akaike's information criterion) Criterion, introduced by *Akaike in 1969, for choosing between competing statistical *models. For *categorical data this amounts to choosing the model that minimizes G2 — 2v, where G2 is the *likelihood-ratio goodness-of-fit statistic and v is’
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the number of 'degrees of freedom associated with the model. An alternative, that usually results in the selection of a simpler model, is the Bayesian information criterion (BIC) for which the quantity minimized is G2 - v In n, where In is the 'natural logarithm and n is the 'sample size. The latter criterion is also called the Schwarz criterion. A third alternative is the Hannan-Quinn criterion for which the quantity to be minimized is G2 - 2v ln(In n). See also MALLOWS CP; STEPWISE PROCEDURES.