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Theory for the Bootstrap

  • Jun Shao
  • Dongsheng Tu
Part of the Springer Series in Statistics book series (SSS)

Abstract

The bootstrap is a very convenient and appealing tool for statistical analysis, however, theoretical and/or empirical confirmation should be made of its suitability for the problem at hand. Also, it is important to know the relative performance of the bootstrap versus other existing methods. A general theory for the bootstrap distribution and variance estimation for a given statistic is presented in this chapter. The more delicate problem of constructing bootstrap confidence sets and hypothesis tests will be treated in the next chapter.

Keywords

Convergence Rate Normal Approximation Strong Consistency Edgeworth Expansion Sample Quantile 
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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Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • Jun Shao
    • 1
  • Dongsheng Tu
    • 2
  1. 1.Department of StatisticsUniversity of Wisconsin, MadisonMadisonUSA
  2. 2.Institute of System ScienceAcademia SinicaBeijingPeople’s Republic of China

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