New methods for multiple testing in permutation inference for the
general linear model
release_56uhnqoqsraohak7hw45zjur4q
by
Tomas Mrkvicka, Mari Myllymaki, Naveen Naidu Narisetty
2019
Abstract
Permutation methods are commonly used to test 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 which improve power and robustness and allow to identify the regions of
potential rejection via a graphical output. The proposed methods rely on
sorting the permuted functional test statistics based on pointwise rank
measures. We developed the R package GET which can be used for computation of
the proposed procedures.
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