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21 Bootstrapping Regression Models

21 BootstrappingRegressionModelsBootstrappi ngis a nonparametric approach to statistical inference that substitutes computationfor more traditional distributional assumptions and asymptotic offersa number of advantages: The bootstrap is quite general, although there are some cases in which it fails. Because it does not require distributional assumptions (such as normally distributed errors),the bootstrap can provide more accurate inferences when the data are not well behaved orwhen the sample size is small. It is possible to apply the bootstrap to statistics with sampling distributions that are difficultto derive, even asymptotically.

My principal aim is to explain how to bootstrap regression models (broadly construed to include generalized linear models, etc.), but the topic is best introduced in a simpler context: Suppose that we draw an independent random sample from a large population. 2 For concreteness and

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