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Applied Nonparametric Statistical Methods, Fourth Edition

By Peter Sprent, Nigel C. Smeeton

Series Editor: Chris Chatfield, Bradley. P. Carlin, Martin A. Tanner, James V. Zidek

Chapman and Hall/CRC – 2007 – 544 pages

Series: Chapman & Hall/CRC Texts in Statistical Science

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    978-1-58488-701-0
    March 5th 2007

Description

While preserving the clear, accessible style of previous editions, Applied Nonparametric Statistical Methods, Fourth Edition reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets.

Reorganized and with additional material, this edition begins with a brief summary of some relevant general statistical concepts and an introduction to basic ideas of nonparametric or distribution-free methods. Designed experiments, including those with factorial treatment structures, are now the focus of an entire chapter. The text also expands coverage on the analysis of survival data and the bootstrap method. The new final chapter focuses on important modern developments, such as large sample methods and computer-intensive applications.

Keeping mathematics to a minimum, this text introduces nonparametric methods to undergraduate students who are taking either mainstream statistics courses or statistics courses within other disciplines. By giving the proper attention to data collection and the interpretation of analyses, it provides a full introduction to nonparametric methods.

Reviews

… The greatest strength of this book is that it is written at a level that is perfectly understandable by readers with only a course or two of introductory-level statistics. As such, it is appropriate for use as either a textbook for a first course in nonparametric methods for undergraduate statistics majors or as a reference for practitioners in other fields. It is also quite suitable as a supplementary statistics textbook for graduate students … . Key concepts are taught using worked-out examples from a variety of fields. … a worthwhile choice for either an introductory-level textbook or a self-study reference for nonspecialists. The writing is very accessible and not weighted down by any mathematics beyond the grasp of the intended audience. …

Psychometrika, Vol. 75, No. 3, September 2010

… this book has an effective organization and covers a wider scope of non-parametric methods than former editions. Therefore, I believe that this book can serve its intended audience.

Journal of the Royal Statistical Society, Series A, Vol. 173, Issue 1, January 2010

Most fourth editions look surprisingly similar to the third editions. Applied Nonparametric Statistical Methods is an exception. Sprent and Smeeton have taken an accessible and well-regarded work and expanded, reorganized, and improved on it. … Sprent and Smeeton offer a strong connection with respect to the how and why of the techniques. … The book’s major strength is its prioritization of coverage. The authors take painstaking care to inculcate an understanding of the appropriate use of nonparametric methods, as well as an appreciation for their application over a wide range of fields. The examples are well chosen, and the variety should ensure that every reader finds at least some of the problems interesting. … As a competitor to the texts by Conover (1999), Gibbons and Chakraborti (2004), Higgins (2004), and Wasserman (2006), Applied Nonparametric Statistical Methods more than holds its own. The combination of clear writing and comprehensive coverage make it an excellent introductory text. …

Technometrics, Vol. 51, No. 2, May 2009

…The chapters have been substantially reorganized, and new material is provided on methods related to factorial designs and time-to-event data. An entirely new chapter, ‘Modern Nonparametrics,’ closes the text with a variety of topics … the worked examples are thoroughly and meticulously done … constant mention is made of the available software (e.g., StatXact, R, Minitab, SPSS) to conduct specific procedures. … solutions to selected end-of-chapter exercises are annotated and quite helpful. Overall, this is a solid choice for a first course in nonparametric statistics for undergraduates.

Journal of the American Statistical Association, Vol. 104, No. 487, September 2009

… expands coverage on the analysis of survival data and the bootstrap method. … the new edition also focuses on some modern developments. The formal testing procedures are illustrated in a nice way with realistic examples leading to final conclusions, comments, and a discussion… The book has a clear style with well-organized material. The book works well as a reference book for users of nonparametric methods in different research areas. It is also a good textbook for undergraduate courses in statistics as well as courses for students majoring in other disciplines.

—Hannu Oja, International Statistical Review, Vol. 27, No. 1, 2008

Praise for the Third Edition

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 analysing their data.

Statistics in Medicine, 2004

Contents

PREFACE

SOME BASIC CONCEPTS

Basic Statistics

Populations and Samples

Hypothesis Testing

Estimation

Ethical Issues

FUNDAMENTALS OF NONPARAMETRIC METHODS

A Permutation Test

Binomial Tests

Order Statistics and Ranks

Exploring Data

Efficiency of Nonparametric Procedures

Computers and Nonparametric Methods

Further Reading

LOCATION INFERENCE FOR SINGLE SAMPLES

Layout of Examples

Continuous Data Samples

Inferences about Medians Based on Ranks

The Sign Test

Use of Alternative Scores

Comparing Tests and Robustness

Fields of Application

Summary

OTHER SINGLE-SAMPLE INFERENCES

Other Data Characteristics

Matching Samples to Distributions

Inferences for Dichotomous Data

Tests Related to the Sign Test

A Runs Test for Randomness

Angular Data

Fields of Application

Summary

METHODS FOR PAIRED SAMPLES

Comparisons in Pairs

A Less Obvious Use of the Sign Test

Power and Sample Size

Fields of Application

Summary

METHODS FOR TWO INDEPENDENT SAMPLES

Centrality Tests and Estimates

The Median Test

Normal Scores

Tests for Equality of Variance

Tests for a Common Distribution

Power and Sample Size

Fields of Application

Summary

BASIC TESTS FOR THREE OR MORE SAMPLES

Comparisons with Parametric Methods

Centrality Tests for Independent Samples

The Friedman Quade and Page Tests

Binary Responses

Tests for Heterogeneity of Variance

Some Miscellaneous Considerations

Fields of Application

Summary

ANALYSIS OF STRUCTURED DATA

Factorial Treatment Structures

Balanced 2 × 2 Factorial Structures

The Nature of Interactions

Alternative Approaches to Interactions

Cross-Over Experiments

Specific and Multiple Comparison Tests

Fields of Application

Summary

Exercises

ANALYSIS OF SURVIVAL DATA

Special Features of Survival Data

Modified Wilcoxon Tests

Savage Scores and the Log-Rank Transformation

Median Tests for Sequential Data

Choice of Tests

Fields of Application

Summary

CORRELATION AND CONCORDANCE

Correlation in Bivariate Data

Ranked Data for Several Variables

Agreement

Fields of Application

Summary

BIVARIATE LINEAR REGRESSION

Fitting Straight Lines

Fields of Application

Summary

CATEGORICAL DATA

Categories and Counts

Nominal Attribute Categories

Ordered Categorical Data

Goodness-of-fit Tests for Discrete Data

Extension of McNemar's Test

Fields of application

Summary

ASSOCIATION IN CATEGORICAL DATA

The Analysis of Association

Some Models for Contingency Tables

Combining and Partitioning of Tables

A Legal Dilemma

Power

Fields of Application

Summary

ROBUST ESTIMATION

When Assumptions Break Down

Outliers and Influence

The Bootstrap

M-estimators and Other Robust Estimators

Fields of Application

Summary

MODERN NONPARAMETRICS

A Change in Emphasis

Density Estimation

Regression

Logistic Regression

Multivariate Data

New Methods for Large Data Sets

Correlations within Clusters

Summary

Exercises appear in each chapter.

APPENDIX 1

APPENDIX 2

REFERENCES

INDEX

Name: Applied Nonparametric Statistical Methods, Fourth Edition (Hardback)Chapman and Hall/CRC 
Description: By Peter Sprent, Nigel C. SmeetonSeries Editor: Chris Chatfield, Bradley. P. Carlin, Martin A. Tanner, James V. Zidek. While preserving the clear, accessible style of previous editions, Applied Nonparametric Statistical Methods, Fourth Edition reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data...
Categories: Statistical Theory & Methods, Statistics for the Biological Sciences, Statistics & Computing