Nonparametric Statistical Tests: A Computational Approach describes classical nonparametric tests, as well as novel and little-known methods such as the Baumgartner-Weiss-Schindler and the Cucconi tests. The book presents SAS and R programs, allowing readers to carry out the different statistical methods, such as permutation and bootstrap tests. The author considers example data sets in each chapter to illustrate methods. Numerous real-life data from various areas, including the bible, and their analyses provide for greatly diversified reading.
The book covers:
- Nonparametric two-sample tests for the location-shift model, specifically the Fisher-Pitman permutation test, the Wilcoxon rank sum test, and the Baumgartner-Weiss-Schindler test
- Permutation tests, location-scale tests, tests for the nonparametric Behrens-Fisher problem, and tests for a difference in variability
- Tests for the general alternative, including the (Kolmogorov-)Smirnov test, ordered categorical, and discrete numerical data
- Well-known one-sample tests such as the sign test and Wilcoxon’s signed rank test, a modification suggested by Pratt (1959), a permutation test with original observations, and a one-sample bootstrap test are presented.
- Tests for more than two groups, the following tests are described in detail: the Kruskal-Wallis test, the permutation F test, the Jonckheere-Terpstra trend test, tests for umbrella alternatives, and the Friedman and Page tests for multiple dependent groups
- The concepts of independence and correlation, and stratified tests such as the van Elteren test and combination tests
- The applicability of computer-intensive methods such as bootstrap and permutation tests for non-standard situations and complex designs
Although the major development of nonparametric methods came to a certain end in the 1970s, their importance undoubtedly persists. What is still needed is a computer assisted evaluation of their main properties. This book closes that gap.
Table of Contents
Introduction and Overview
Nonparametric tests for the location problem
Tests in case of heteroscedasticity
Tests for the general alternative
Ordered categorical and discrete data
The conservativeness of permutation tests
Further examples for the comparison of two groups
One-sample tests and tests for paired data
Tests for more than two groups
Independence and correlation
Stratified studies and combination of p-values
Estimation and confidence intervals
Appendix. Nonparametric tests in R
there are substantial amounts of SAS code in the body of the work, and a briefer account of R code in an appendix. … While many standard statistical software packages include the classic nonparametric procedures, this volume presents many recent ones that have not found their way into most software yet, hence the need to include code for these techniques. … The writing is clear and concise … The work is more free than most recent works from the kinds of errors spell checkers do not find. Highly recommended to anyone familiar with the classic nonparametric tests who wants an update (and extensive bibliography) concerning recent results.
—Robert W. Hayden, MAA Reviews, March 2012