Resampling Methods for Dependent Data

  • S. N. Lahiri

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

Table of contents

  1. Front Matter
    Pages I-XIV
  2. S. N. Lahiri
    Pages 17-43
  3. S. N. Lahiri
    Pages 73-113
  4. S. N. Lahiri
    Pages 115-144
  5. S. N. Lahiri
    Pages 145-173
  6. S. N. Lahiri
    Pages 175-197
  7. S. N. Lahiri
    Pages 199-220
  8. S. N. Lahiri
    Pages 221-240
  9. S. N. Lahiri
    Pages 241-259
  10. S. N. Lahiri
    Pages 281-338
  11. Back Matter
    Pages 339-377

About this book


This is a book on bootstrap and related resampling methods for temporal and spatial data exhibiting various forms of dependence. Like the resam­ pling methods for independent data, these methods provide tools for sta­ tistical analysis of dependent data without requiring stringent structural assumptions. This is an important aspect of the resampling methods in the dependent case, as the problem of model misspecification is more preva­ lent under dependence and traditional statistical methods are often very sensitive to deviations from model assumptions. Following the tremendous success of Efron's (1979) bootstrap to provide answers to many complex problems involving independent data and following Singh's (1981) example on the inadequacy of the method under dependence, there have been several attempts in the literature to extend the bootstrap method to the dependent case. A breakthrough was achieved when resampling of single observations was replaced with block resampling, an idea that was put forward by Hall (1985), Carlstein (1986), Kiinsch (1989), Liu and Singh (1992), and others in various forms and in different inference problems. There has been a vig­ orous development in the area of res amp ling methods for dependent data since then and it is still an area of active research. This book describes various aspects of the theory and methodology of resampling methods for dependent data developed over the last two decades. There are mainly two target audiences for the book, with the level of exposition of the relevant parts tailored to each audience.


Bootstrapping Resampling Ringe STATISTICA permutation tests statistics

Authors and affiliations

  • S. N. Lahiri
    • 1
  1. 1.Department of StatisticsIowa State UniversityAmesUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York 2003
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4419-1848-2
  • Online ISBN 978-1-4757-3803-2
  • Series Print ISSN 0172-7397
  • Buy this book on publisher's site
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