7.3 Relative Model-Data Fit at Test Level (Cont’d)
Let be the number of model parameters, several information criteria can be defined:
Akaike (1974) Information Criterion (AIC) adjusts the -2 log likelihood by twice the number of parameters in the model: Schwarz (1978) Bayesian Criterion (BIC) has a stronger penalty than the AIC for overparametrized models, and adjusts the -2 log likelihood by the number of parameters times the log of the number of cases. It is also known as the Bayesian Information Criterion. Bozdogan (1987) Consistent Akaike’s Information Criterion (CAIC) has a stronger penalty than the AIC for overparametrized models, and adjusts the -2 log likelihood by the number of parameters times one plus the log of the number of cases. As the sample size increases, the CAIC converges to the BIC. The sample-size-adjusted BIC (SABIC) is proposed by Sclove (1987) to reduce the penalty in BIC.