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
Citations
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Statistical tools for general association testing and control of false discoveries in group testing
TL;DR: This dissertation describes RankCover, a new non-parametric association test for association between two variables that measures the concentration of paired ranked points and proposes a new method to control the false discovery rate (FDR) for grouped hypothesis data.
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Validation of Association
Bogdan Ćmiel,Teresa Ledwina +1 more
TL;DR: This paper investigates how a new function-valued measure of dependence, the quantile dependence function, can be used to construct tests for independence and to provide an easily interpretable diagnostic plot of existing departures from the null model.
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Some tests of independence based on maximum mean discrepancy and ranks of nearest neighbors
Angshuman Roy,Anil K. Ghosh +1 more
TL;DR: In this article, the authors use the ideas of maximum mean discrepancy and ranks of nearest neighbors to propose some tests of independence among multiple random vectors of arbitrary dimensions, which can outperform the existing tests in various examples.
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Systematically Exploring Associations among Multivariate Data
TL;DR: A statistical tool named the neighbor correlation coefficient (nCor), which is based on a new idea that measures the local continuity of the reordered data points to quantify the strength of the global association between variables, is proposed.
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Some copula‐based tests of independence among several random variables having arbitrary probability distributions
TL;DR: This article proposes some copula‐based tests of independence which are invariant under strictly monotone transformations of the variables, and they can be used for continuous, discrete, or even for ordinal variables.
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