Abstract
The problem of evaluating the goodness of statistical models is important in various fields of statistical science. Akaike's information criterion [1] provides a useful tool for evaluating models estimated by the method of maximum likelihood. By extending Akaike's basic idea, several attempts have been made to relax the assumptions imposed on AIC and obtained information criteria which may be applied to various types of statistical models. In this paper, we briefly review the definition of the information criteria GIC and EIC, and then show some of their modifications which can yield more refined results than previously proposed criteria.
