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

Keywords

Permutation (music), Inference, Computer science, Random permutation, Mathematics

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