A novel scale-space approach for multinormality testing and the k-sample problem in the high dimension low sample size scenario

Kristian Hindberg, Jan Hannig and Fred Godtliebsen

PLOS ONE, 2019, vol. 14, issue 1, 1-20

Abstract: Two classical multivariate statistical problems, testing of multivariate normality and the k-sample problem, are explored by a novel analysis on several resolutions simultaneously. The presented methods do not invert any estimated covariance matrix. Thereby, the methods work in the High Dimension Low Sample Size situation, i.e. when n ≤ p. The output, a significance map, is produced by doing a one-dimensional test for all possible resolution/position pairs. The significance map shows for which resolution/position pairs the null hypothesis is rejected. For the testing of multinormality, the Anderson-Darling test is utilized to detect potential departures from multinormality at different combinations of resolutions and positions. In the k-sample case, it is tested whether k data sets can be said to originate from the same unspecified discrete or continuous multivariate distribution. This is done by testing the k vectors corresponding to the same resolution/position pair of the k different data sets through the k-sample Anderson-Darling test. Successful demonstrations of the new methodology on artificial and real data sets are presented, and a feature selection scheme is demonstrated.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0211044

DOI: 10.1371/journal.pone.0211044

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