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dc.contributor.authorLopes, RHC-
dc.contributor.authorHobson, PR-
dc.contributor.authorReid, ID-
dc.identifier.citationJournal of Physics: Conference Series. 120(2008) 042019, Jun 2008en
dc.description.abstractGoodness-of-fit statistics measure the compatibility of random samples against some theoretical or reference probability distribution function. The classical one-dimensional Kolmogorov-Smirnov test is a non-parametric statistic for comparing two empirical distributions which defines the largest absolute difference between the two cumulative distribution functions as a measure of disagreement. Adapting this test to more than one dimension is a challenge because there are 2^d-1 independent ways of ordering a cumulative distribution function in d dimensions. We discuss Peacock's version of the Kolmogorov-Smirnov test for two-dimensional data sets which computes the differences between cumulative distribution functions in 4n^2 quadrants. We also examine Fasano and Franceschini's variation of Peacock's test, Cooke's algorithm for Peacock's test, and ROOT's version of the two-dimensional Kolmogorov-Smirnov test. We establish a lower-bound limit on the work for computing Peacock's test of Omega(n^2.lg(n)), introducing optimal algorithms for both this and Fasano and Franceschini's test, and show that Cooke's algorithm is not a faithful implementation of Peacock's test. We also discuss and evaluate parallel algorithms for Peacock's test.en
dc.format.extent2276534 bytes-
dc.rightsCopyright © Institute of Physics and IOP Publishing Limited 2008en
dc.subjectStatistical testsen
dc.subjectComputer scienceen
dc.titleComputationally efficient algorithms for the two-dimensional Kolmogorov-Smirnov testen
dc.typeResearch Paperen
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Research Papers

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