8.2 AIC y BIC

The Akaike information criterion (AIC) and the Bayesian information criterion (BIC) make use of the Log-likelihood, which is the logarithm of maximum likelihood, and subtract a term proportional to the number of parameters in the model. maximum likelihood, and subtract a term proportional to the number of parameters in the model.

• The \(AIC\) is given by:

\[AIC = -2 \log L + 2(K+1)\]

The best models are those with the lowest AIC value.

• The \(BIC\) is given by:

\[BIC = -2\log L + \log(n)(K+1)\] The model with the lowest \(BIC\) value is considered the best at explaining the data with the minimum number of parameters. with the minimum number of parameters.

• In our example:

Criterio	Valor
Akaike criterion	117.0828
Schwarz criterion	124.8983