A Note On the Uni cation of the Akaike Information Criterion
نویسنده
چکیده
SUMMARY In order to measure the distance between a robust function evaluated under the true regression model and under a tted model, we propose a generalized Kullback-Leibler information. Using this generalization we have developed three robust model selection criteria, AICR , AICCR and AICCR, that allow the selection of candidate models that not only t the majority of the data, but also take into account non-normally distributed errors. These two criteria, AICR and AICCR, can unify most existing Akaike information criteria; three examples of such uniication are given. Simulation studies are presented to illustrate the relative performance of each criterion.
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