Akaike Information Criterion

Abstract

The Akaike Information Criterion is one of a range of ways of choosing between different types of models that seek an appropriate trade-off between goodness of fit and model complexity. The more complicated a model is the better generally will be its apparent goodness of fit, if the parameters are selected to optimise goodness of fit, but this does not necessarily make it a ‘better’ model overall for identifying how new data might behave.