Permutation methods can provide exact control of false positives and allow the use of non-standard statistics, making only weak assumptions about the data. With the availability of fast and inexpensive computing, their main limitation would be some lack of flexibility to work with arbitrary experimental designs. In this paper we report on results on approximate permutation methods that are more flexible with respect to the experimental design and nuisance variables, and conduct detailed simul...Expand abstract
- Publication status:
- Peer review status:
- Peer reviewed
- Publisher's version
- Copyright holder:
- Anderson M. Winkler et al.
- Copyright date:
© 2014 Anderson M. Winkler et al. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).
articletitle: Permutation inference for the general linear model
copyright: Copyright © 2014 The Authors. Published by Elsevier Inc.