Permutation Tests for Complex Data

Timely update of a popular edition on permutation testing with numerous case studies included throughout
The newly revised and updated Second Edition of Permutation Tests for Complex Data describes permutation tests from the point of view of experimental design, with methodological details and illustrating the process of devising an appropriate permutation test through case studies. In addition to the text, this book includes two open source packages for permutation tests in Python and R which include a comprehensive code base to implement common permutation tests as well as code to implement each of the book's case studies.
The focus of this book is the permutation approach to a variety of univariate and multivariate problems of hypothesis testing in a typical nonparametric framework. The book examines the most up-to-date methodologies of univariate and multivariate permutation testing, includes real case studies from both experimental and observational studies, and presents and discusses solutions to the most important and frequently encountered real problems in multivariate analyses.
Written by two highly qualified authors in the field of nonparametrics and applied statistics, Permutation Tests for Complex Data includes information on sample topics including: - Theory of one-dimensional and multi-dimensional permutation tests, covering test statistics, arguments for selecting permutation tests, and examples of one-sample and multi-sample problems
- Multiplicity control and closed testing, covering raw and adjusted p-values, the MinP Bonferroni-Holm procedure, and weighted methods for controlling FWE and FDR
- Multivariate categorical variables, covering stochastic ordering, tests on moments for ordered variables, and heterogeneity comparisons
- NPC tests for survival analysis and shape analysis, covering analysis of PERC data and analysis with correlated landmarks

Presenting a thorough overview of permutation testing with both formal description and proofs, Permutation Tests for Complex Data is an excellent introduction to permutation tests for graduate-level statistics or data science courses and will be ideal as a handbook for researchers hoping to use the open source code.