In this paper, we derived and investigated the Adjusted Network Information Criterion (ANIC) criterion, based on Kullback’s symmetric divergence, which has been designed to be an asymptotically unbiased estimator of the expected Kullback-Leibler information of a fitted model. The ANIC improves model selection in more sample sizes than does the NIC.
Udomboso, Christopher Godwin; Amahia, Godwin Nwazu; and Dontwi, Isaac Kwame
"An Adjusted Network Information Criterion for Model Selection in Statistical Neural Network Models,"
Journal of Modern Applied Statistical Methods: Vol. 15
, Article 26.
Available at: http://digitalcommons.wayne.edu/jmasm/vol15/iss2/26