Nonparametric Statistical Tests: A Computational Approach

Markus Neuhauser

October 23, 2017 by Chapman and Hall/CRC
Reference - 248 Pages - 12 B/W Illustrations
ISBN 9781138114104 - CAT# K35311

USD$79.95

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Features

  • Provides a modern and accessible overview of computationally-intensive nonparametric statistical methods
  • Accessible to both statisticians and non-statisticians
  • Focuses on permutation and bootstrap methods
  • Presents real examples to illustrate application of the methods
  • Includes SAS programs for applying all the methods, with R code in an appendix

Summary

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.

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