A novel AIC variant for linear regression models based on a bootstrap correction
The Akaike information criterion, AIC, and its corrected version, AIC c 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
|Collections||ANU Research Publications|
|Source:||Proceedings of IEEE Workshop on Machine Learning for Signal Processing 2008|
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|03_Seghouane_A_novel_AIC_variant_for_linear_2008.pdf||656.41 kB||Adobe PDF||Request a copy|
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