Improved estimators of Kullback–Leibler information for autoregressive model selection in small samples
小样本自回归模型选择中 Kullback–Leibler 信息的改进估计
A new estimator, AICl, of the Kuliback-Leibler information is proposed for Gaussian autoregressive time series model selection. The expected information is decomposed into two terms, the first being estimated without bias. The second term is a penalty function which depends only weakly on the model parameters, and hence can be tabulated by simulation from a white noise process. The estimated information, AICl, is very nearly unbiased when the model is either correct or…