Applied Nonparametric Statistical Methods, Third Edition

Published:
September 07, 2000 by Chapman and Hall/CRC - 480 Pages
Author(s):
Peter Sprent, Dundee University, Scotland; Nigel C. Smeeton, Guy's King & St. Thomas School of Medicine, London, UK
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ISBN 9781584881452
Cat# C1453
 

Features

  • Follows the same easy-to-read pattern of the previous editions
  • Keeps mathematics to a minimum to promote clear understanding
  • Provides 120 worked examples from a wide range of applications, drawing attention to practical details
  • Contains an account of up-to-date computing developments
  • Reflects the shift in emphasis from hypothesis testing to estimation
  • Stresses relationships between topics throughout the text

    • Summary

    This new edition follows the basic easy-to-digest pattern that was so well received by users of the earlier editions. The authors substantially update and expand Applied Nonparametric Statistical Methods to reflect changing attitudes towards applied statistics, new developments, and the impact of more widely available and better statistical software.

    The book takes into account computing developments since the publication of the popular Second Edition, rearranging the material in a more logical order, and introducing new topics. It emphasizes better use of significance tests and focuses greater attention on medical and dental applications. Applied Nonparametric Statistical Methods: Third Edition explains the rationale of procedures with a minimum of mathematical detail, making it not only an outstanding textbook, but also an up-to-date reference for professionals who do their own statistical analyses.
    New in the Third Edition:

  • Expanded coverage of topics - such as ethical considerations and calculation of power and of sample sizes needed
  • Refers to a wide variety of statistical packages - such as StatXact, Minitab, Testimate, S-plus, Stata, and SPSS
  • Includes sections on the analysis of angular data, the use of capture-recapture methods, the measurement of agreement between observers, runs tests, and regression diagnostics.

    • Table of Contents

    PREFACE
    INTRODUCING NONPARAMETRIC METHODS
    Basic Statistics
    Samples and Populations
    Hypothesis Tests
    Estimation
    Ethical Issues
    Computers and Nonparametric Methods
    Further Reading
    CENTRALITY INFERENCE FOR SINGLE SAMPLES
    Using Measurement Data
    Inferences about Medians Based on Ranks
    The Sign Test
    Transformation of Ranks
    Asymptotic Results
    Robustness
    OTHER SINGLE-SAMPLE INFERENCE
    Inferences for Dichotomous Data
    Tests Related to the Sign Test
    Matching Samples to Distributions
    Angular Data
    A Runs Test for Randomness
    METHODS FOR PAIRED SAMPLES
    Comparisons in Pairs
    A Less Obvious Use of the Sign Test
    Power and Sample Size
    METHODS FOR TWO INDEPENDENT SAMPLES
    Centrality Tests and Estimates
    Rank Based Tests
    The Median Test
    Normal Scores
    Tests for Survival Data
    Asymptotic Approximation
    Power and Sample Size
    Tests for Equality of Variance
    Tests for a Common Distribution
    THREE OR MORE SAMPLES
    Comparisons with Parametric Methods
    Centrality Tests for Independent Samples
    Centrality Tests for Related Samples
    More Detailed Treatment Comparisons
    Tests for Heterogeneity of Variance
    Some Miscellaneous
    Considerations
    CORRELATION AND CONCORDANCE
    Correlation and Bivariate Data
    Ranked Data for Several Variables
    Agreement
    REGRESSION
    Bivariate Linear Regression
    Multiple Regression
    Nonparametric Regression Models
    Other Multivariate Data Problems
    CATEGORICAL DATA
    Categories and Counts
    Nominal Attribute Categories
    Ordered Categorical Data
    Goodness-of-Fit Tests for Discrete Data
    Extension of McNemar's
    Test
    ASSOCIATION IN CATEGORICAL DATA
    The Analysis of Association
    Some Models for Contingency Tables
    Combining and Partitioning of Tables
    Power
    ROBUST ESTIMATION
    When Assumptions Break Down
    Outliers and Influence
    The Bootstrap
    M-Estimators and Other Robust Estimators
    APPENDIX
    REFERENCES
    SO

    Editorial Reviews

    "Strengths of this text certainly include its organization and writing style. Applied Nonparametric Statistical Methods provides a very clear exposition of modern nonparametric methods. Many students and practitioners will find it an excellent resource and reference for nonparametric statistics."
    -Technometrics, 2003

    "… extremely valuable for statisticians as well as for researchers in applied fields. … This well-written book is highly recommended for those readers who want to get a feeling for the nonparametric methods which they apply when analyzing their data."
    - Edgar Brunner, in Statistics in Medicine, 2004

    About the Second Edition…
    "Nicely laid out for practitioners with minimal theory, very deliberate explanations, and lots of illustrations."
    -Technometrics

    "This is the third edition of a very good book on nonparametric statistics…goes far beyond being a compendium of useful methods. It aims at promoting understanding as well. Good statistical practice is exemplified throughout… Particularly commendable are their discussions of multiple comparisons and conditioning in the two by two contingency table… If I could only have one book on nonparametric methods, this would be my choice. It is highly recommended."
    -Short Book Reviews of the ISI, Vol. 21, No. 2, August, 2001

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