How Biased is the Apparent Error Rate of a Prediction Rule?
研究了回归模型表观错误率低估真实错误率的偏差，给出了简单估计方法，适用于指数族线性模型和多种预测误差度量，并比较了Cp、交叉验证、自助法、AIC等方法。
Abstract A regression model is fitted to an observed set of data. How accurate is the model for predicting future observations? The apparent error rate tends to underestimate the true error rate because the data have been used twice, both to fit the model and to check its accuracy. We provide simple estimates for the downward bias of the apparent error rate. The theory applies to general exponential family linear models and general measures of prediction error. Special attention is given to the case of logistic regression on binary data, with error rates measured by the proportion of misclassified cases. Several connected ideas are compared: Mallows's Cp , cross-validation, generalized cross-validation, the bootstrap, and Akaike's information criterion.