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Subsampling

  • Textbook
  • © 1999

Overview

Authors:
  1. Dimitris N. Politis

    1. Department of Mathematics, University of California, San Diego, La Jolla, USA

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  2. Joseph P. Romano

    1. Department of Statistics, Stanford University, Stanford, USA

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  3. Michael Wolf

    1. Departamento de Estadistica y Econometria, Universidad Carlos III de Madrid, Getafe, Spain

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

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-xv
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  2. Basic Theory

    1. Front Matter

      Pages 1-1
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    2. Bootstrap Sampling Distributions

      • Dimitris N. Politis, Joseph P. Romano, Michael Wolf
      Pages 3-38

    3. Subsampling in the I.I.D. Case

      • Dimitris N. Politis, Joseph P. Romano, Michael Wolf
      Pages 39-64

    4. Subsampling for Stationary Time Series

      • Dimitris N. Politis, Joseph P. Romano, Michael Wolf
      Pages 65-100

    5. Subsampling for Nonstationary Time Series

      • Dimitris N. Politis, Joseph P. Romano, Michael Wolf
      Pages 101-119

    6. Subsampling for Random Fields

      • Dimitris N. Politis, Joseph P. Romano, Michael Wolf
      Pages 120-137

    7. Subsampling Marked Point Processes

      • Dimitris N. Politis, Joseph P. Romano, Michael Wolf
      Pages 138-158

    8. Confidence Sets for General Parameters

      • Dimitris N. Politis, Joseph P. Romano, Michael Wolf
      Pages 159-170

  3. Extensions, Practical Issues, and Applications

    1. Front Matter

      Pages 171-171
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    2. Subsampling with Unknown Convergence Rate

      • Dimitris N. Politis, Joseph P. Romano, Michael Wolf
      Pages 173-187

    3. Choice of the Block Size

      • Dimitris N. Politis, Joseph P. Romano, Michael Wolf
      Pages 188-212

    4. Extrapolation, Interpolation, and Higher-Order Accuracy

      • Dimitris N. Politis, Joseph P. Romano, Michael Wolf
      Pages 213-252

    5. Subsampling the Mean with Heavy Tails

      • Dimitris N. Politis, Joseph P. Romano, Michael Wolf
      Pages 253-269

    6. Subsampling the Autoregressive Parameter

      • Dimitris N. Politis, Joseph P. Romano, Michael Wolf
      Pages 270-290

    7. Subsampling Stock Returns

      • Dimitris N. Politis, Joseph P. Romano, Michael Wolf
      Pages 291-314

  4. Back Matter

    Pages 315-348
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Keywords

About this book

Since Efron's profound paper on the bootstrap, an enormous amount of effort has been spent on the development of bootstrap, jacknife, and other resampling methods. The primary goal of these computer-intensive methods has been to provide statistical tools that work in complex situations without imposing unrealistic or unverifiable assumptions about the data generating mechanism. The primary goal of this book is to lay some of the foundation for subsampling methodology and related methods.

Reviews

"The book is one of the most comprehensive texts in the subsampling realm and provides a solid background for researchers working in the related areas of statistics."
V.V. Fedorov in "Short Book Reviews", Vol. 21/1, April 2001

Authors and Affiliations

  • Department of Mathematics, University of California, San Diego, La Jolla, USA

    Dimitris N. Politis

  • Department of Statistics, Stanford University, Stanford, USA

    Joseph P. Romano

  • Departamento de Estadistica y Econometria, Universidad Carlos III de Madrid, Getafe, Spain

    Michael Wolf

Bibliographic Information

  • Book Title: Subsampling

  • Authors: Dimitris N. Politis, Joseph P. Romano, Michael Wolf

  • Series Title: Springer Series in Statistics

  • DOI: https://doi.org/10.1007/978-1-4612-1554-7

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 1999

  • Hardcover ISBN: 978-0-387-98854-2Published: 13 August 1999

  • Softcover ISBN: 978-1-4612-7190-1Published: 30 October 2012

  • eBook ISBN: 978-1-4612-1554-7Published: 06 December 2012

  • Series ISSN: 0172-7397

  • Series E-ISSN: 2197-568X

  • Edition Number: 1

  • Number of Pages: XV, 348

  • Topics: Statistical Theory and Methods

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