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A two-sample distribution-free test for functional data with application to a diffusion tensor imaging study of multiple sclerosis
2016
Journal of the Royal Statistical Society, Series C: Applied Statistics
Motivated by an imaging study, this paper develops a nonparametric testing procedure for testing the null hypothesis that two samples of curves observed at discrete grids and with noise have the same underlying distribution. The objective is to formally compare white matter tract profiles between healthy individuals and multiple sclerosis patients, as assessed by conventional diffusion tensor imaging measures. We propose to decompose the curves using functional principal component analysis of a
doi:10.1111/rssc.12130
pmid:27041772
pmcid:PMC4812165
fatcat:5n3ljca6jbaingnqwqhgm6qag4