AIC (Akaike's information criterion)

"Akaike's Information Criterion is a criterion for selecting among nested econometric models."
About, Inc. (2006)

"An index used in a number of areas as an aid to choosing between competing models. It is defined as

-2L_m + 2m
where L_m is the maximized log-likelihood and m is the number of parameters in the model. The index takes into account both the statistical goodness of fit and the number of parameters that have to be estimated to achieve this particular degree of fit, by imposing a penalty for increasing the number of parameters. Lower values of the index indicate the preferred model, that is, the one with the fewest parameters that still provides an adequate fit to the data."
Everitt (1998), The Cambridge Dictionary of Statistics

"Akaike (1973) defined the most well-known criterion as AIC = - ln L + p, where L is the likelihood for an estimated model with p parameters."
Hjorth (1994)

"When a model involving q parameters is fitted to data, the criterion is defined as -2L_q + 2q, where L_q is the maximised log likelihood. Akaike suggested maximising the numbers of parameters. It was originally proposed for time-series models, but has also been used in regression."
Marriott (1990), A Dictionary of Statistical Terms

"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 v is the number of degrees of freedom associated with the model."
Upton and Cook (2002)

"The Akaike information criterion (AIC) (pronounced, approximately, ah-kah-ee-kay), developed by Professor Hirotugu Akaike (?? ??) in 1971 and proposed in 1974, is a statistical model fit measure."
Wikipedia (2006)