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An exact distribution‐free test comparing two multivariate distributions based on adjacency
Author(s) -
Rosenbaum Paul R.
Publication year - 2005
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2005.00513.x
Subject(s) - mathematics , test statistic , matching (statistics) , sign test , combinatorics , disjoint sets , bipartite graph , multivariate normal distribution , statistics , distribution (mathematics) , statistic , multivariate statistics , adjacency list , statistical hypothesis testing , mathematical analysis , graph , wilcoxon signed rank test , mann–whitney u test
Summary. A new test is proposed comparing two multivariate distributions by using distances between observations. Unlike earlier tests using interpoint distances, the new test statistic has a known exact distribution and is exactly distribution free. The interpoint distances are used to construct an optimal non‐bipartite matching, i.e. a matching of the observations into disjoint pairs to minimize the total distance within pairs. The cross‐match statistic is the number of pairs containing one observation from the first distribution and one from the second. Distributions that are very different will exhibit few cross‐matches. When comparing two discrete distributions with finite support, the test is consistent against all alternatives. The test is applied to a study of brain activation measured by functional magnetic resonance imaging during two linguistic tasks, comparing brains that are impaired by arteriovenous abnormalities with normal controls. A second exact distribution‐free test is also discussed: it ranks the pairs and sums the ranks of the cross‐matched pairs.