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Robust nonparametric tests of general linear model coefficients: A comparison of permutation methods and test statistics

2019
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NeuroImage
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Statistical inference in neuroimaging research often involves testing the significance of regression coefficients in a general linear model. In many applications, the researcher assumes a model of the form Y=α+Xβ+Zγ+ε, where Y is the observed brain signal, and X and Z contain explanatory variables that are thought to be related to the brain signal. The goal is to test the null hypothesis H0:β=0 with the nuisance parameters γ included in the model. Several nonparametric (permutation) methods

doi:10.1016/j.neuroimage.2019.116030
pmid:31330243
pmcid:PMC6765412
fatcat:27rmvdrckbfvjcvqdqvozyaapu