Inadmissibility of the corrected Akaike information criterion

Abstract

For the multivariate linear regression model with unknown covariance, the corrected Akaike information criterion is the minimum variance unbiased estimator of the expected Kullback–Leibler discrepancy. In this study, based on the loss estimation framework, we show its inadmissibility as an estimator of the Kullback–Leibler discrepancy itself, instead of the expected Kullback–Leibler discrepancy. We provide improved estimators of the Kullback–Leibler discrepancy that work well in reduced-rank situations and examine their performance numerically.