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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1166

Title: The two-dimensional Kolmogorov-Smirnov test
Authors: Lopes, RHC
Reid, ID
Hobson, PR
Keywords: Statistics
Algorithm
Non-parametric
Publication Date: 2007
Publisher: Proceedings of Science
Citation: XI International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Nikhef, Amsterdam, the Netherlands, April 23-27, 2007
Abstract: Goodness-of-fit statistics measure the compatibility of random samples against some theoretical 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 2d −1 independent ways of defining a cumulative distribution function when d dimensions are involved. In this paper three variations on the Kolmogorov-Smirnov test for multi-dimensional data sets are surveyed: Peacock’s test [1] that computes in O(n3); Fasano and Franceschini’s test [2] that computes in O(n2); Cooke’s test that computes in O(n2). We prove that Cooke’s algorithm runs in O(n2), contrary to his claims that it runs in O(nlgn). We also compare these algorithms with ROOT’s version of the Kolmogorov-Smirnov test.
URI: http://bura.brunel.ac.uk/handle/2438/1166
Appears in Collections:School of Engineering and Design Research papers
Electronic and Computer Engineering

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