%0 Journal Article
%T An Akaike Criterion based on Kullback Symmetric Divergence in the Presence of Incomplete-Data
%A B Hafidi
%A A Mkhadri
%J Afrika Statistika
%D 2007
%I 
%X 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.
%U http://www.ajol.info/index.php/afst/article/view/46864