The Covariance Inflation Criterion for Adaptive Model Selection
提出一种新的模型选择准则，通过预测与响应的平均协方差调整训练误差，适用于回归、分类及多种预测方法，并给出自适应过程的有效参数数度量。
Summary We propose a new criterion for model selection in prediction problems. The covariance inflation criterion adjusts the training error by the average covariance of the predictions and responses, when the prediction rule is applied to permuted versions of the data set. This criterion can be applied to general prediction problems (e.g. regression or classification) and to general prediction rules (e.g. stepwise regression, tree-based models and neural nets). As a by-product we obtain a measure of the effective number of parameters used by an adaptive procedure. We relate the covariance inflation criterion to other model selection procedures and illustrate its use in some regression and classification problems. We also revisit the conditional bootstrap approach to model selection.