An Adjusted Network Information Criterion for Model Selection in Statistical Neural Network Models

Authors

Christopher Godwin Udomboso, University of Ibadan, Ibadan, NigeriaFollow
Godwin Nwazu Amahia, University of Ibadan, Ibadan, NigeriaFollow
Isaac Kwame Dontwi, Kwame Nkrumah University of Science and Technology, Kumasi, GhanaFollow

Abstract

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.

DOI

10.22237/jmasm/1478003040