Multivariate regression model selection from small samples using Kullback's symmetric divergence

Abstract

The Kullback Information Criterion, KIC, and its univariate bias-corrected version, KICc, are two new developed criteria for model selection. The two criteria can be viewed as estimators of the expected Kullback symmetric divergence and they have a fixed bias corrected term. In this paper, a new small sample model selection criterion for multivariate regression models is developed. The proposed criterion is named KICvc, where the notation "vc" stands for vector correction, and it can be considered as an extension of KIC for multivariate regression models. KICvc adjusts KIC to be an unbiased estimator for the variant of the Kullback symmetric divergence, assuming that the true model is correc..