Bootstrap Prediction Intervals for Regression
本文提出用Bootstrap方法为线性回归模型构造预测区间，无需假设抽样分布，在小样本中近似达到名义置信水平，并通过蒙特卡洛实验与其他非参数方法对比。
Abstract Bootstrap prediction intervals provide a nonparametric measure of the probable error of forecasts from a standard linear regression model. These intervals approximate the nominal probability content in small samples without requiring specific assumptions about the sampling distribution. Empirical measures of the prediction error rate motivate the choice of these intervals, which are calculated by an application of the bootstrap. The intervals are contrasted to other nonparametric procedures in several Monte Carlo experiments. Asymptotic invariance properties are also investigated.