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Nonparametric Hypothesis Testing: Rank and Permutation Methods with Applications in R

Nonparametric Hypothesis Testing: Rank and Permutation Methods with Applications in R

by Luigi Salmaso, Marco Marozzi, Livio Corain, Stefano Bonnini
Publisher: John Wiley & Sons
Release Date: August 2014
ISBN: 9781118763483
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Book Description

A novel presentation of rank and permutation tests, with accessible guidance to applications in R

Nonparametric testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. This book summarizes traditional rank techniques and more recent developments in permutation testing as robust tools for dealing with complex data with low sample size.

Key Features:

  • Examines the most widely used methodologies of nonparametric testing.

  • Includes extensive software codes in R featuring worked examples, and uses real case studies from both experimental and observational studies.

  • Presents and discusses solutions to the most important and frequently encountered real problems in different fields.

    • Features a supporting website (www.wiley.com/go/hypothesis_testing) containing all of the data sets examined in the book along with ready to use R software codes.

    Nonparametric Hypothesis Testing combines an up to date overview with useful practical guidance to applications in R, and will be a valuable resource for practitioners and researchers working in a wide range of scientific fields including engineering, biostatistics, psychology and medicine.

    Table of Contents

    1. Presentation of the book
    2. Preface
    3. Notation and abbreviations
    4. 1 One- and two-sample location problems, tests for symmetry and tests on a single distribution
      1. 1.1 Introduction
      2. 1.2 Nonparametric tests
      3. 1.3 Univariate one-sample tests
      4. 1.4 Multivariate one-sample tests
      5. 1.5 Univariate two-sample tests
      6. 1.6 Multivariate two-sample tests
      7. References
    5. 2 Comparing variability and distributions
      1. 2.1 Introduction
      2. 2.2 Comparing variability
      3. 2.3 Jointly comparing central tendency and variability
      4. 2.4 Comparing distributions
      5. References
    6. 3 Comparing more than two samples
      1. 3.1 Introduction
      2. 3.2 One-way ANOVA layout
      3. 3.3 Two-way ANOVA layout
      4. 3.4 Pairwise multiple comparisons
      5. 3.5 Multivariate multisample tests
      6. References
    7. 4 Paired samples and repeated measures
      1. 4.1 Introduction
      2. 4.2 Two-sample problems with paired data
      3. 4.3 Repeated measures tests
      4. References
    8. 5 Tests for categorical data
      1. 5.1 Introduction
      2. 5.2 One-sample tests
      3. 5.3 Two-sample tests on proportions or 2 × 2 contingency tables
      4. 5.4 Tests for R × × C contingency tables
      5. References
    9. 6 Testing for correlation and concordance
      1. 6.1 Introduction
      2. 6.2 Measuring correlation
      3. 6.3 Tests for independence
      4. 6.4 Tests for concordance
      5. References
    10. 7 Tests for heterogeneity
      1. 7.1 Introduction
      2. 7.2 Statistical heterogeneity
      3. 7.3 Dominance in heterogeneity
      4. 7.4 Two-sided and multisample test
      5. References
    11. Appendix A Selected critical values for the null distribution of the peak- known Mack–Wolfe statistic
    12. Appendix B Selected critical values for the null distribution of the peak- unknown Mack–Wolfe statistic
    13. Appendix C Selected upper-tail probabilities for the null distribution of the Page L statistic
    14. Appendix D R functions and codes
    15. Index
    16. Series
    17. End User License Agreement