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AIC stands for the Akaike Information Criterion, which is one technique used to select the best model from a class of models using a penalized likelihood. A smaller AIC implies a better model.

$\mathit{AIC} = 2k - 2\ln(L)$

where $k$ is the number of parameters in the statistical model, and $L$ is the maximized value of the likelihood function for the estimated model.

${AICc} = AIC + \frac{2k(k + 1)}{n - k - 1}$

AICc is AIC with a correction for finite sample sizes, where $n$ denotes the sample size. Thus, AICc is AIC with a greater penalty for extra parameters.

AIC was introduced by Hirotugu Akaike in his seminal 1973 paper "Information Theory and an Extension of the Maximum Likelihood Principle" (in: B. N. Petrov and F. Csaki, eds., 2nd International Symposium on Information Theory, Akademia Kiado, Budapest, pp. 267{281).

References:

"Information Theory and an Extension of the Maximum Likelihood Principle" (starts on page 610).