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Nonparametric Statistical Tests

Markus Neuhauser
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A01=Markus Neuhauser
Ansari Bradley Test
approximate
Approximate Permutation Test
asymptotic
Asymptotic Relative Efficiency
Author_Markus Neuhauser
Behrens-Fisher problem
bootstrap
bootstrap resampling
Bootstrap Samples
Bootstrap Test
Category=PBT
Category=PS
closed
computational nonparametric data analysis
Data Set
distribution
Empirical Distribution Functions
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
Estimation and confidence intervals
exact
Exact Likelihood Ratio Test
Exact Permutation Test
Fisher's Combination Test
Fisher’s Combination Test
hypothesis
Jonckheere Terpstra Test
Kruskal-Wallis analysis
Lepage Test
Linear Rank Statistic
Location Shift Model
Ni N1i
Nonparametric tests for the location problem
permutation
permutation methods
Permutation Null Distribution
Permutation Test
Procedure Npar1way
rank-based inference
samples
SAS Procedure Freq
Smirnov Test
stratified statistical tests
Stratified studies and combination of p-values
Tests in case of heteroscedasticity
The conservativeness of permutation tests
Van Elteren Test
Var Difference
Wilcoxon Signed Rank Test
WMW Test

Product details

  • ISBN 9781138114104
  • Weight: 362g
  • Dimensions: 156 x 234mm
  • Publication Date: 23 Oct 2017
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Description

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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