A note on the unification of the Akaike information criterion |
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Authors: | P. Shi,& C-L. Tsai |
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Affiliation: | Peking University, Beijing, People's Republic of China,;University of California, Davis, USA |
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Abstract: | To measure the distance between a robust function evaluated under the true regression model and under a fitted model, we propose 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 fit the majority of the data but also take into account non-normally distributed errors. The AICR* and AICCR criteria can unify most existing Akaike information criteria; three examples of such unification are given. Simulation studies are presented to illustrate the relative performance of each criterion. |
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Keywords: | Akaike information criterion Corrected Akaike information criterion Generalized Akaike information criteria Kullback–Leibler information Robust model selection |
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