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Open AccessJournal ArticleDOI

A consistent multivariate test of association based on ranks of distances

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TLDR
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.

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Proceedings ArticleDOI

Detecting Dependency Between Discrete Random Variables and Application

TL;DR: These novel dependency measures have been applied to supervised feature selection to show their usefulness and are used to design new correlation functions, in order to compare them with the ones in literature.
Posted Content

MultiFIT: Multivariate Multiscale Framework for Independence Tests

Shai Gorsky, +1 more
TL;DR: A data-adaptive coarse-to-fine testing procedure that completes a fraction of the univariate tests, which are judged to be prone to containing evidence for dependency by exploiting the spatial features of dependency structures, and provides a finite-sample theoretical guarantee for the exact validity of the adaptive procedure.
Posted Content

Equitability of Dependence Measure

TL;DR: In this article, the authors introduce a new definition of equitability of a dependence measure, i.e., power-equitable (weakequitable) and show by simulation that HHG and Copula Dependence Coefficient (CDC) are weakequitable.
Posted Content

Ball: An R package for detecting distribution difference and association in metric spaces

TL;DR: In this paper, a publicly available R package Ball is provided to implement Ball statistical test procedures for K-sample distribution comparison and test of mutual independence in metric spaces, which extend the test procedure for two sample distribution comparison in Euclidean or Hilbert spaces.
Journal ArticleDOI

On some consistent tests of mutual independence among several random vectors of arbitrary dimensions

TL;DR: This article proposes two general recipes, one based on inter-point distances and the other based on linear projections, for multivariate extensions of these univariate tests, and carries out extensive numerical studies to compare the empirical performance of these proposed methods with the state-of-the-art methods.
References
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Journal Article

R: A language and environment for statistical computing.

R Core Team
- 01 Jan 2014 - 
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

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
Journal ArticleDOI

The Analysis of Variance.

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