Transcription of 21 Bootstrapping Regression Models
{{id}} {{{paragraph}}}
Example: dental hygienist
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. It is relatively simple to apply the bootstrap to complex data-collection plans (such asstratified and clustered samples).
21.1. Bootstrapping Basics 589 y∗ p∗(y∗) 6 .25 −3 .25 5 .25 3 .25 Note that E∗(Y∗) = all y∗ y∗p(y∗) = 2.75 = Y and V∗(Y∗) = [y∗ −E∗(Y∗)]2p(y∗)= 12.187 = 3 4 S2 = n−1 n S2 Thus, the expectation of Y∗ is just the sample mean of Y, and the variance of Y∗ is [except for the factor (n−1)/n, which is trivial in larger samples] the sample variance of Y.
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: