The Akaike information criterion, AIC, and its corrected version, AICc are two methods for selecting normal linear regression models. Both criteria were designed as estimators of the expected Kullback-Leibler information between the model generating the data and the approximating candidate model. In this paper, two new corrected variants of AIC are derived for the purpose of small sample linear regression model selection. The proposed variants of AIC are based on asymptotic approximation of...[Show more]
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