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Test of independence plays a fundamental role in many statistical techniques.
On the independence of k sets of normally distributed statistical variables
SS Wilks · 1935
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Some theorems on quadratic forms applied in the study of analysis of variance problems, i. effect of inequality of variance in the one-way classification
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Multivariate analysis
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Fourier Analysis on Groups
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Sampling techniques for kernel methods
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Fast Monte-Carlo algorithms for finding low-rank approximations
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Multivariate nonparametric tests of independence
Sara Taskinen, Hannu Oja, and Ronald H Randles · 2005
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Random projection, margins, kernels, and feature-selection
Avrim Blum · 2006
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
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Measuring and testing dependence by correlation of distances
Gábor J Székely, Maria L Rizzo, and Nail K Bakirov · 2007
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Brownian distance covariance
Gábor J Székely and Maria L Rizzo · 2009
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Distributions of angles in random packing on spheres
T Tony Cai, Jianqing Fan, and Tiefeng Jiang · 2013
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The randomized dependence coefficient
David Lopez-Paz, Philipp Hennig, and Bernhard Schölkopf · 2013
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Distance covariance in metric spaces
Russell Lyons · 2013
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On quantifying dependence: a framework for developing interpretable measures
Matthew Reimherr and Dan L Nicolae · 2013
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Equivalence of distance-based and RKHS-based statistics in hypothesis testing
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A comparison of efficient approximations for a weighted sum of chi-squared random variables
Dean A Bodenham and Niall M Adams · 2014
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Consistent distribution-free k k -sample and independence tests for univariate random variables
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Fast computing for distance covariance
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