Regression and time series model selection in small samples

作者：Clifford M. Hurvich, Chih‐Ling Tsai · 发表于：Biometrika · 年份：1989 · DOI：10.1093/biomet/76.2.297 · 被引用次数：6483 · 研究领域：Control Systems and Identification、Statistical Methods and Inference、Neural Networks and Applications

A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series models. The correction is of particular use when the sample size is small, or when the number of fitted parameters is a moderate to large fraction of the sample size. The corrected method, called AICC, is asymptotically efficient if the true model is infinite dimensional. Furthermore, when the true model is of finite dimension, AICC is found to provide better model order choices than any other asymptotically efficient method. Applications to nonstationary autoregressive and mixed autoregressive moving average time series models are also discussed.