Permutation Tests and the Bootstrap

  • Kenneth Lange
Part of the Statistics and Computing book series (SCO)


In this chapter we discuss two techniques, permutation testing and the bootstrap, of immense practical value. Both techniques involve random resampling of observed data and liberate statisticians from dubious model assumptions and large sample requirements. Both techniques initially met with considerable intellectual resistance. The notion that one can conduct hypothesis tests or learn something useful about the properties of estimators and confidence intervals by resampling data was alien to most statisticians of the past. The computational demands of permutation testing and bootstrapping alone made them unthinkable. These philosophical and practical objections began to crumble with the advent of modern computing.


Permutation Test Importance Sampling Edgeworth Expansion Multivariate Observation Antenna Length 
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.


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© Springer New York 2010

Authors and Affiliations

  1. 1.Departments of Biomathematics, Human Genetics, and Statistics David Geffen School of MedicineUniversity of California, Los AngelesLos AngelesUSA

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