On the Bias and Variability of Bootstrap and Cross-Validation Estimates of Error Rate in Discrimination Problems
通过局部替代模型，研究了判别问题中自助法和交叉验证估计总体误差率的偏差和变异性差异，发现当总体接近时，自助法变异性更小但偏差更大。
Simulation studies have shown that bootstrap and cross-validation estimators of aggregate error rate in discrimination problems have different properties, the former having less variability but greater bias. We show by use of a local alternative model that the main differences in bias and variability emerge only when the populations are close. In this context the bootstrap method has less variability but an order of magnitude greater bias than cross-validation.