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

ABSTRACT: 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 have been proposed for this problem, and each method uses some variant of the F ratio as the test statistic. However, recent research suggests that the F ratio can produce invalid permutation tests of H₀:β=0 when the ε terms are heteroscedastic (i.e., have non-constant variance), w

SUBMITTER: Helwig NE 

PROVIDER: S-EPMC6765412 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature