© 2011 – Chapman and Hall/CRC
248 pages | 12 B/W Illus.
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:
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.
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
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