Feasible Cross-validatory Model Selection for General Stationary Processes

@inproceedings{Racine1997FeasibleCM,
  title={Feasible Cross-validatory Model Selection for General Stationary Processes},
  author={Jeff Racine},
  year={1997}
}
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-validationfunction for least-squares based estimators can be expressed in a form which can enormously impact on the amount of calculation required. The standard… CONTINUE READING
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