This paper investigates and evaluates an extension of the Akaike information criterion, KIC, which is an approximately unbiased estimator for a risk function based on the Kullback symmetric divergence. KIC is based on the observed-data empirical log-likelihood which may be problematic to compute in the presence of incompletedata. We derive and investigate a variant of KIC criterion for model selection in settings where the observed-data is incomplete. We examine the performance of our criterion relative to other well known criteria in a large simulation study based on bivariate normal model and bivariate regression modeling.
"An Akaike criterion based on Kullback symmetric divergence in the presence of incomplete-data." Afr. Stat. 2 (1) 1 - 21, 2007. https://doi.org/10.4314/afst.v2i1.46864