Bootstrap and Cross-Validation Estimates of the Prediction Error for Linear Regression Models
线性回归预测误差的Bootstrap与交叉验证估计
Different estimates of the mean squared error of prediction for linear regression models are derived by the bootstrap and cross-validation approaches. A comparison is made under normal error distributions, especially by the biases and the mean square errors. The results indicate that the bias corrected bootstrap estimator is best unbiased and should be the first choice, while its simulated variant has approximately the same behaviour. On the other hand, if only a comparison…