FEASIBLE CROSS-VALIDATORY MODEL SELECTION FOR GENERAL STATIONARY PROCESSES
一般平稳过程的可行交叉验证模型选择
Cross-validation is a method used to estimate the expected prediction error of a model. Such estimates may be of interest in themselves, but their use for model selection is more common. Unfortunately, cross-validation is viewed as being computationally expensive in many situations. In this paper it is shown that the h-block cross-validation function for least-squares based estimators can be expressed in a form which enormously impact on the amount of calculation required.…