Applied Nonparametric Statistical Methods, Third Edition
By Peter Sprent, Nigel C. Smeeton
Series Editor: Chris Chatfield, Jim Zidek
Published December 12th 2010 by Chapman and Hall/CRC – 480 pages
Description
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:
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
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
Name: Applied Nonparametric Statistical Methods, Third Edition (eBook) – Chapman and Hall/CRC
Description: By Peter Sprent, Nigel C. SmeetonSeries Editor: Chris Chatfield, Jim Zidek. 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...
Categories: Statistical Theory & Methods, Statistics for the Biological Sciences