Permutation inference for the general linear model

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 simulations to identify the best method for settings that are typical for imaging research scenarios. We present a generic framework for permutation inference for complex general linear models (GLMS) when the errors are…

• G
  GlaxoSmithKline
• WT
  Wellcome Trust

  Awards: 098369/Z/12/Z, 100309/Z/12/Z
• UO
  University of Oxford
• UD
  Università degli Studi di Padova
• NI
  National Institutes of Health

  Awards: R01 EB015611-01, 100309/Z/12/Z, R01 EB015611, NIH R01
• MR
  Medical Research Council

  Awards: MR/J014257/1, G0900908, 098369/Z/12/Z, MR/J014257/1