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Model comparison plays a central role in statistical learning and chemometrics. Performances of models need to be assessed using a given criterion based on which models can be compared. To our knowledge, there exist a variety of criteria that can be applied for model assessment, such as Akaike’s information criterion (AIC) [1], Bayesian information criterion (BIC) [2], deviance information criterion (DIC),Mallow’s Cp statistic, cross validation [3-6] and so on. There is a large body of literature that is devoted to these criteria. With the aid of a chosen criterion, different models can be compared. For example, a model with a smaller AIC or BIC is preferred if AIC or BIC are chosen for model assessment.

Model Population Analysis for Statistical Model Comparison