Permutation testing for thick data when the number of variables is much greater than the sample size: recent developments and some recommendations

DOI10.1007/s00180-022-01218-3OpenAlexW4220746034MaRDI QIDQ6104404

Arne C. Bathke, Rosa Arboretti, Luigi Salmaso, Riccardo Ceccato, Patrick B. Langthaler

Publication date: 15 June 2023

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-022-01218-3

zbMATH Keywords

permutation testingmultivariate testingrank-based methodsthick data

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Cites Work

• A nonparametric version of Wilks' lambda -- asymptotic results and small sample approximations
• Nonparametric methods for unbalanced multivariate data and many factor levels
• Computation of multivariate normal and \(t\) probabilities
• How to compare small multivariate samples using nonparametric tests
• Nonparametric methods in multivariate factorial designs for large number of factor levels
• Multidimensional Stochastic Ordering and Associated Random Variables
• Dependency and truncated forms of combinations in multivariate combination-based permutation tests and ordered categorical variables
• A Nonparametric Version of the Bartlett-Nanda-Pillai Multivariate Test. Asymptotics, Approximations, and Applications
• Choosing between methods of combining $p$-values
• Combining Independent Tests of Significance

This page was built for publication: Permutation testing for thick data when the number of variables is much greater than the sample size: recent developments and some recommendations