The model selection criterion AICu

✍ Scribed by Allan McQuarrie; Robert Shumway; Chih-Ling Tsai

• Publisher: Elsevier Science
• Year: 1997
• Tongue: English
• Weight: 431 KB
• Volume: 34
• Category: Article
• ISSN: 0167-7152
• DOI: 10.1016/s0167-7152(96)00192-7

✦ Synopsis

For regression and time series model selection, obtained a bias correction Akaike information criterion, AICc, which provides better model order choices than the Akaike information criterion, AIC . In this paper, we propose an alternative improved regression model selection criterion, AICu, which is an approximate unbiased estimator of Kullback-Leibler information. We show that AICu is neither a consistent nor an efficient criterion. Our simulation studies indicate that the behavior of AICu is a compromise between that of efficient (AICc) and consistent (BIC, Akaike,1978) criteria. Specifically, AICu performs better than AICc for moderate to large sample sizes except when the true model is of infinite order. In addition, it outperforms BIC except when a true model exists and the sample size is large.