Akaike information criterion

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. It quantifies the relative goodness-of-fit of various previously derived statistical models, given a sample of data. It uses a rigorous framework of information analysis based on the concept of entropy. The driving idea behind the AIC is to examine the complexity of the model together with goodness of its fit to the sample data, and to produce a measure which balances between the two. A model with many parameters will provide a very good fit to the data, but will have few degrees of freedom and be of limited utility. This balanced approach discourages overfitting.

See also

• time series analysis, Kullback-Leibler divergence, Bayesian information criteria
• Occam's Razor

External links

• http://www.garfield.library.upenn.edu/classics1981/A1981MS54100001.pdf