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The Jackknife and Bootstrap

  • Book
  • © 1995

Overview

Authors:
  1. Jun Shao

    1. Department of Statistics, University of Wisconsin, Madison, Madison, USA

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  2. Dongsheng Tu

    1. Institute of System Science, Academia Sinica, Beijing, People’s Republic of China

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    You can also search for this author in PubMed   Google Scholar

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

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xvii
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  2. Introduction

    • Jun Shao, Dongsheng Tu
    Pages 1-22

  3. Theory for the Jackknife

    • Jun Shao, Dongsheng Tu
    Pages 23-70

  4. Theory for the Bootstrap

    • Jun Shao, Dongsheng Tu
    Pages 71-128

  5. Bootstrap Confidence Sets and Hypothesis Tests

    • Jun Shao, Dongsheng Tu
    Pages 129-189

  6. Computational Methods

    • Jun Shao, Dongsheng Tu
    Pages 190-231

  7. Applications to Sample Surveys

    • Jun Shao, Dongsheng Tu
    Pages 232-282

  8. Applications to Linear Models

    • Jun Shao, Dongsheng Tu
    Pages 283-330

  9. Applications to Nonlinear, Nonparametric, and Multivariate Models

    • Jun Shao, Dongsheng Tu
    Pages 331-385

  10. Applications to Time Series and Other Dependent Data

    • Jun Shao, Dongsheng Tu
    Pages 386-415

  11. Bayesian Bootstrap and Random Weighting

    • Jun Shao, Dongsheng Tu
    Pages 416-446

  12. Back Matter

    Pages 447-517
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Keywords

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.

Authors and Affiliations

  • Department of Statistics, University of Wisconsin, Madison, Madison, USA

    Jun Shao

  • Institute of System Science, Academia Sinica, Beijing, People’s Republic of China

    Dongsheng Tu

Bibliographic Information

  • Book Title: The Jackknife and Bootstrap

  • Authors: Jun Shao, Dongsheng Tu

  • Series Title: Springer Series in Statistics

  • DOI: https://doi.org/10.1007/978-1-4612-0795-5

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 1995

  • Hardcover ISBN: 978-0-387-94515-6Published: 21 July 1995

  • Softcover ISBN: 978-1-4612-6903-8Published: 04 October 2012

  • eBook ISBN: 978-1-4612-0795-5Published: 06 December 2012

  • Series ISSN: 0172-7397

  • Series E-ISSN: 2197-568X

  • Edition Number: 1

  • Number of Pages: XVII, 517

  • Topics: Applications of Mathematics

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