Permutation inference for the general linear model
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Summary
This paper presents a generic framework for permutation inference for complex general linear models (glms) when the errors are exchangeable and/or have a symmetric distribution, and shows that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios.
- Type
- article
- Published
- 2014-05-01
- Cited by
- 3,516
- References
- 106
- Access
- Open access
- OpenAlex
- https://openalex.org/W2126693856
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:11349921
Keywords
Permutation (music), Inference, Computer science, Random permutation, Mathematics
References
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- Permutation Tests for Linear Models
- Comparison of Fourier and wavelet resampling methods
- A comparison of random field theory and permutation methods for the statistical analysis of MEG data
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- The approximate randomization test as an alternative to the F test in analysis of variance
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