Praise for previous editions:
" a classic with a long history." - Statistical Papers
"The fact that the first edition of this book was published in 1971 [is] testimony to the books success over a long period." - ISI Short Book Reviews
" one of the best books available for a theory course on nonparametric statistics. very well written and organized recommended for teachers and graduate students." - Biometrics
" There is no competitor for this book and its comprehensive development and application of nonparametric methods. Users of one of the earlier editions should certainly consider upgrading to this new edition." - Technometrics
" Useful to students and research workers a good textbook for a beginning graduate-level course in nonparametric statistics." - Journal of the American Statistical Association
Since its first publication in 1971, Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametrics. The Sixth Edition carries on this tradition and incorporates computer solutions based on R.
Features
Covers the most commonly used nonparametric procedures
States the assumptions, develops the theory behind the procedures, and illustrates the techniques using realistic examples from the social, behavioral, and life sciences
Presents tests of hypotheses, confidence-interval estimation, sample size determination, power, and comparisons of competing procedures
Includes an Appendix of user-friendly tables needed for solutions to all data-oriented examples
Gives examples of computer applications based on R, MINITAB, STATXACT, and SAS
Lists over 100 new references
Nonparametric Statistical Inference, Sixth Edition, has been thoroughly revised and rewritten to make it more readable and reader-friendly. All of the R solutions are new and make this book much more useful for applications in modern times. It has been updated throughout and contains 100 new citations, including some of the most recent, to make it more current and useful for researchers.
1. Introduction and Fundamentals
2. Order Statistics, Quantiles, and Coverages
3. Tests of Randomness
4. Tests of Goodness of Fit
5. One-Sample and Paired-Sample Procedures
6. The General Two-Sample Problem
7. Linear Rank Statistics and the General Two-Sample Problem
8. Linear Rank Tests for the Location Problem
9. Linear Rank Tests for the Scale Problem
10. Tests of the Equality of k Distributions
11. Measures of Association for Bivariate Samples
12. Measures of Association in Multiple Classifications
13. Asymptotic Relative Efficiency
14. Analysis of Count Data
15. Summary
