• David Ruppert
  • David S. Matteson
Part of the Springer Texts in Statistics book series (STS)


Finding a single set of estimates for the parameters in a statistical model is not enough. An assessment of the uncertainty in these estimates is also needed. Standard errors and confidence intervals are common methods for expressing uncertainty. In the past, it was sometimes difficult, if not impossible, to assess uncertainty, especially for complex models. Fortunately, the speed of modern computers, and the innovations in statistical methodology inspired by this speed, have largely overcome this problem. In this chapter we apply a computer simulation technique called the “bootstrap” or “resampling” to find standard errors and confidence intervals. The bootstrap method is very widely applicable and will be used extensively in the remainder of this book. The bootstrap is one way that modern computing has revolutionized statistics.


  1. Chernick, M. R. (2007) Bootstrap Methods: A Guide for Practitioners and Researchers, 2nd ed., Wiley-Interscience, Hoboken, NJ.CrossRefGoogle Scholar
  2. Davison, A. C., and Hinkley, D. V. (1997) Bootstrap Methods and Their Applications, Cambridge University Press, Cambridge.CrossRefGoogle Scholar
  3. Efron, B. (1979) Bootstrap methods: Another look at the jackknife. Annals of Statistics, 7, 1–26.CrossRefzbMATHMathSciNetGoogle Scholar
  4. Efron, B., and Tibshirani, R. (1993) An Introduction to the Bootstrap, Chapman & Hall, New York.CrossRefzbMATHGoogle Scholar
  5. Good, P. I. (2005) Resampling Methods: A Practical Guide to Data Analysis, 3rd ed., Birkhauser, Boston.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • David Ruppert
    • 1
  • David S. Matteson
    • 2
  1. 1.Department of Statistical Science and School of ORIECornell UniversityIthacaUSA
  2. 2.Department of Statistical Science Department of Social StatisticsCornell UniversityIthacaUSA

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