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Permutation inference for the general linear model.

Abstract:

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...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1016/j.neuroimage.2014.01.060

Authors


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Institution:
University of Oxford
Department:
Oxford, MSD, Clinical Neuroscience
Role:
Author
More by this author
Institution:
University of Oxford
Department:
Oxford, MSD, Clinical Neurosciences
Role:
Author
More by this author
Institution:
University of Oxford
Department:
Oxford, MSD, Clinical Neuroscience
Role:
Author
More by this author
Institution:
University of Oxford
Department:
Oxford, MSD, Clinical Neuroscience
Role:
Author
More by this author
Institution:
University of Oxford
Department:
Oxford, MSD, Clinical Neuroscience
Role:
Author
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Funding agency for:
Winkler, AM
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Funding agency for:
Winkler, AM
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Funding agency for:
Winkler, AM
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Funding agency for:
Webster, MA
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Funding agency for:
Smith, SM
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Publisher:
Elsevier Inc. Publisher's website
Journal:
Neuroimage Journal website
Volume:
92
Pages:
381-397
Chapter number:
C
Publication date:
2014-05-15
DOI:
EISSN:
1095-9572
ISSN:
1095-9572
URN:
uuid:ff7332f0-89d1-4aa7-a23a-988e2d58442e
Source identifiers:
449134
Local pid:
pubs:449134

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