Overview

AIC


Show Summary Details

Quick Reference

(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 G2−2ν, where G2 is the likelihood-ratio goodness-of-fit statistic and ν is 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−ν ln n, where ln 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 G2−2ν ln(ln n). See also Mallows Cp; stepwise procedures.

Subjects: Probability and Statistics.


Reference entries

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.