Various Model Evaluation Criteria
So far in this book, we have considered model selection and evaluation criteria from both an information-theoretic point of view and a Bayesian approach. The AIC-type criteria were constructed as estimators of the Kullback–Leibler information between a statistical model and the true distribution generating the data or equivalently the expected log-likelihood of a statistical model. In contrast, the Bayes approach for selecting a model was to choose the model with the largest posterior probability among a set of candidate models.
There are other model evaluation criteria based on various different points of view. This chapter describes cross-validation, generalized cross-validation, final predictive error (FPE), Mallows’ C p , the Hannan–Quinn criterion, and ICOMP. Cross-validation also provides an alternative approach to estimate the Kullback–Leibler information. We show that the cross-validation estimate is asymptotically equivalent to AIC-type criteria in a general setting.
KeywordsFinal Prediction Error Leibler Information Predictive Mean Square Error Bias Correction Term Large Posterior Probability
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