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Resampling Methods

  • Gareth James
  • Daniela Witten
  • Trevor Hastie
  • Robert Tibshirani
Chapter
Part of the Springer Texts in Statistics book series (STS, volume 103)

Abstract

Resampling methods are an indispensable tool in modern statistics. They involve repeatedly drawing samples from a training set and refitting a model of interest on each sample in order to obtain additional information about the fitted model. For example, in order to estimate the variability of a linear regression fit, we can repeatedly draw different samples from the training data, fit a linear regression to each new sample, and then examine the extent to which the resulting fits differ. Such an approach may allow us to obtain information that would not be available from fitting the model only once using the original training sample.

Keywords

Test Error Bootstrap Estimate Test Error Rate Statistical Learning Method LOOCV Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Gareth James
    • 1
  • Daniela Witten
    • 2
  • Trevor Hastie
    • 3
  • Robert Tibshirani
    • 3
  1. 1.Department of Information and Operations ManagementUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Department of BiostatisticsUniversity of WashingtonSeattleUSA
  3. 3.Department of StatisticsStanford UniversityStanfordUSA

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