Statistics Model selection Concept
Akaike information criterion
Summary
The Akaike information criterion (AIC) scores a fitted statistical model by its maximized likelihood, with a penalty for the number of parameters. Among candidate models, one prefers the model with the smallest AIC. It rests on an estimate of the expected Kullback–Leibler divergence between the true law and the fitted model, and it aims at good prediction.
Further reading
About this page
| Tag | CMAX3KF9H0 |
|---|---|
| URI | m2://CMAX3KF9H0 |
| Kind | concept |
| Writing | W0 stub |
| MSC | 62B10 |
| Same as |
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