Regression and Time Series Model Selection in Small Samples
针对小样本或参数较多的情况，推导了Akaike信息准则（AIC）的偏差修正方法（AICC），在真实模型为无限维时渐近有效，有限维时模型阶数选择优于其他渐近有效方法，并讨论了非平稳自回归和混合自回归移动平均模型的应用。
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.