New methods for multiple testing in permutation inference for the general linear model
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12510%2F22%3A43902760" target="_blank" >RIV/60076658:12510/22:43902760 - isvavai.cz</a>
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/10.1002/sim.9236" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/sim.9236</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/sim.9236" target="_blank" >10.1002/sim.9236</a>
Alternative languages
Result language
angličtina
Original language name
New methods for multiple testing in permutation inference for the general linear model
Original language description
Permutation methods are commonly used to test the significance of regressors of interest in general linear models (GLMs) for functional (image) data sets, in particular for neuroimaging applications as they rely on mild assumptions. Permutation inference for GLMs typically consists of three parts: choosing a relevant test statistic, computing pointwise permutation tests, and applying a multiple testing correction. We propose new multiple testing methods as an alternative to the commonly used maximum value of test statistics across the image. The new methods improve power and robustness against inhomogeneity of the test statistic across its domain. The methods rely on sorting the permuted functional test statistics based on pointwise rank measures; still, they can be implemented even for large data. The performance of the methods is demonstrated through a designed simulation experiment and an example of brain imaging data. We developed the R package GET, which can be used for the computation of the proposed procedures.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/GA19-04412S" target="_blank" >GA19-04412S: New approaches to modeling and statistics of random sets</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Statistics in Medicine
ISSN
0277-6715
e-ISSN
1097-0258
Volume of the periodical
41
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
22
Pages from-to
276-297
UT code for WoS article
000710114700001
EID of the result in the Scopus database
2-s2.0-85117597162