Informational complexity criteria for regression models (original) (raw)

This paper pursues three objectives in the context of multiple regression models: • To give a rationale for model selection criteria which combine a badness-of-fit term (such as minus twice the maximum log likelihood) with a measure of complexity of a model. We show that the ICOMP criterion introduced by Bozdogan can be seen as an approximation to the sum of two Kullback-Leibler distances, and that a criterion related to ICOMP arises as an approximation to the posterior expectation of a certain utility.