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Permutation Methods

A Distance Function Approach

  • Book
  • © 2007

Overview

Authors:
  1. Paul W. Mielke

    1. Department of Statistics, Colorado State University, Fort Collins

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    You can also search for this author in PubMed   Google Scholar

  2. Kenneth J. Berry

    1. Department of Sociology, Colorado State University, Fort Collins

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    You can also search for this author in PubMed   Google Scholar

  • Makes a variety of powerful data analytic tools easily available to practitioners
  • New material has been added to the second edition

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

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Table of contents (9 chapters)

  1. Front Matter

    Pages I-XVII
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  2. Introduction

    • Paul W. Mielke Jr., Kenneth J. Berry
    Pages 1-10

  3. Description of MRPP

    • Paul W. Mielke Jr., Kenneth J. Berry
    Pages 11-68

  4. Additional MRPP Applications

    • Paul W. Mielke Jr., Kenneth J. Berry
    Pages 69-123

  5. Description of MRBP

    • Paul W. Mielke Jr., Kenneth J. Berry
    Pages 125-169

  6. Regression Analysis, Prediction, and Agreement

    • Paul W. Mielke Jr., Kenneth J. Berry
    Pages 171-262

  7. Goodness-of-Fit Tests

    • Paul W. Mielke Jr., Kenneth J. Berry
    Pages 263-282

  8. Contingency Tables

    • Paul W. Mielke Jr., Kenneth J. Berry
    Pages 283-327

  9. Multisample Homogeneity Tests

    • Paul W. Mielke Jr., Kenneth J. Berry
    Pages 329-340

  10. Selected Permutation Studies

    • Paul W. Mielke Jr., Kenneth J. Berry
    Pages 341-383

  11. Back Matter

    Pages 385-439
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Keywords

About this book

Most commonly-used parametric and permutation statistical tests, such as the matched-pairs t test and analysis of variance, are based on non-metric squared distance functions that have very poor robustness characteristics. This second edition places increased emphasis on the use of alternative permutation statistical tests based on metric Euclidean distance functions that have excellent robustness characteristics. These alternative permutation techniques provide many powerful multivariate tests including multivariate multiple regression analyses. In addition to permutation techniques described in the first edition, this second edition also contains various new permutation statistical methods and studies that include resampling multiple contingency table analyses, analysis concerns involving log-linear models with small samples, an exact discrete analog of Fisher’s continuous method for combining P-values that arise from small data sets, multiple dichotomous response analyses, problems regarding Fisher’s Z transformation for correlation analyses, and multivariate similarity comparisons between corresponding multiple categories of two samples.

Reviews

From the Reviews:

"[T]his is a nicely written book that contains many important and useful topics, and I am certan that many pracitioners and researchers will find the new edition beneficial." (Technometrics, May 2008, Vol. 50 No. 2)

“This is a very well-written text that extensively covers permutation-based tests in a general framework. It has been revised and extended by nearly 100 pages since the 2001 edition. …This book is packed with real-data examples and dozens of simulation studies exploring the properties of permutation-based tests and contrasting them with their typical parametric ‘competitors.’ The authors do not shy away from presenting the mathematical underpinnings of the methods, and do so in a very transparent and easy-to-follow manner so there is sufficient detail to implement the methods in your favorite software … . That is not a concern if you are familiar with FORTRAN-77, as the authors have provided over 100 FORTRAN programs an associated datasets for download in Unix-compatible and Windows-compatible format. These well-commented programs are briefly described in Appendix A with subsections organized by chapter. …Permutation Methods is a superb book that is highly recommended.” ( Journal of the American Statistical Association, Dec. 2009, Vol. 104, No. 488)

Authors and Affiliations

  • Department of Statistics, Colorado State University, Fort Collins

    Paul W. Mielke

  • Department of Sociology, Colorado State University, Fort Collins

    Kenneth J. Berry

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