A consistent multivariate test of association based on ranks of distances
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In this paper, the problem of detecting associations between random vectors of any dimension is considered and a powerful test that is applicable in all dimensions and consistent against all alternatives is proposed. But the test has a simple form, is easy to implement, and has good power.Abstract:
SUMMARY We consider the problem of detecting associations between random vectors of any dimension. Few tests of independence exist that are consistent against all dependent alternatives. We propose a powerful test that is applicable in all dimensions and consistent against all alternatives. The test has a simple form, is easy to implement, and has good power.read more
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References
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Journal Article
R: A language and environment for statistical computing.
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Journal ArticleDOI
The Analysis of Variance
TL;DR: In this paper, the basic theory of analysis of variance by considering several different mathematical models is examined, including fixed-effects models with independent observations of equal variance and other models with different observations of variance.
Journal ArticleDOI
Measuring and testing dependence by correlation of distances
TL;DR: Distance correlation is a new measure of dependence between random vectors that is based on certain Euclidean distances between sample elements rather than sample moments, yet has a compact representation analogous to the classical covariance and correlation.
Journal ArticleDOI
Applied smoothing techniques for data analysis : the kernel approach with S-plus illustrations
Adrian Bowman,Adelchi Azzalini +1 more
TL;DR: 1. Density estimation for exploring data 2. D density estimation for inference 3. Nonparametric regression for explore data 4. Inference with nonparametric regressors 5. Checking parametric regression models 6. Comparing regression curves and surfaces