The Jackknife and Bootstrap

  • Jun Shao
  • Dongsheng Tu

Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Jun Shao, Dongsheng Tu
    Pages 1-22
  3. Jun Shao, Dongsheng Tu
    Pages 23-70
  4. Jun Shao, Dongsheng Tu
    Pages 71-128
  5. Jun Shao, Dongsheng Tu
    Pages 129-189
  6. Jun Shao, Dongsheng Tu
    Pages 190-231
  7. Jun Shao, Dongsheng Tu
    Pages 232-282
  8. Jun Shao, Dongsheng Tu
    Pages 283-330
  9. Jun Shao, Dongsheng Tu
    Pages 386-415
  10. Jun Shao, Dongsheng Tu
    Pages 416-446
  11. Back Matter
    Pages 447-517

About this book


The jackknife and bootstrap are the most popular data-resampling meth­ ods used in statistical analysis. The resampling methods replace theoreti­ cal derivations required in applying traditional methods (such as substitu­ tion and linearization) in statistical analysis by repeatedly resampling the original data and making inferences from the resamples. Because of the availability of inexpensive and fast computing, these computer-intensive methods have caught on very rapidly in recent years and are particularly appreciated by applied statisticians. The primary aims of this book are (1) to provide a systematic introduction to the theory of the jackknife, the bootstrap, and other resampling methods developed in the last twenty years; (2) to provide a guide for applied statisticians: practitioners often use (or misuse) the resampling methods in situations where no theoretical confirmation has been made; and (3) to stimulate the use of the jackknife and bootstrap and further devel­ opments of the resampling methods. The theoretical properties of the jackknife and bootstrap methods are studied in this book in an asymptotic framework. Theorems are illustrated by examples. Finite sample properties of the jackknife and bootstrap are mostly investigated by examples and/or empirical simulation studies. In addition to the theory for the jackknife and bootstrap methods in problems with independent and identically distributed (Li.d.) data, we try to cover, as much as we can, the applications of the jackknife and bootstrap in various complicated non-Li.d. data problems.


Bootstrapping Covariance matrix Estimator Factor analysis Generalized linear model Likelihood Monte Carlo method Resampling STATISTICA Time series Uniform integrability Variance linear regression mathematical statistics statistics

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

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York, Inc. 1995
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-6903-8
  • Online ISBN 978-1-4612-0795-5
  • Series Print ISSN 0172-7397
  • Buy this book on publisher's site
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