A Kullback's symmetric divergence criterion with application to linear regression and time series model

Abstract

The Kullback information criterion (KIC) is a recently developed tool for statistical model selection. KIC serves as an asymptotically unbiased estimator of the Kullback symmetric divergence, known as J-divergence. A corrected version for KIC denoted by KICC have been also proposed to correct the bias of KIC. This version tends to overfit when the sample size increases.In this paper we propose an alternative to KICC, the KICU criterion which is unbiased estimator of the Kullback's symmetric divergence. It provides better model choice than KICC for moderate to large sample size. ©2005 IEEE.