Testing independence in high dimensions with sums of rank correlations
DOI10.1214/17-AOS1550zbMath1415.62038arXiv1501.01732OpenAlexW2963530010WikidataQ57566346 ScholiaQ57566346MaRDI QIDQ1747739
Publication date: 27 April 2018
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1501.01732
independenceU-statisticshigh-dimensional statisticsrank correlationsminimax optimalitymartingale central limit theorem
Hypothesis testing in multivariate analysis (62H15) Central limit and other weak theorems (60F05) Order statistics; empirical distribution functions (62G30) Martingales with continuous parameter (60G44)
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- Large-Sample Theory for the Bergsma-Dassios Sign Covariance
- A new test of independence for high-dimensional data
- On Jiang's asymptotic distribution of the largest entry of a sample correlation matrix
- Limiting laws of coherence of random matrices with applications to testing covariance structure and construction of compressed sensing matrices
- Efficient computation of the Bergsma-Dassios sign covariance
- Multivariate nonparametric methods with R. An approach based on spatial signs and ranks.
- Testing independence in high dimensions with sums of rank correlations
- Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
- The asymptotic distributions of the largest entries of sample correlation matrices.
- A necessary test for complete independence in high dimensions using rank-correlations
- The asymptotic distribution and Berry-Esseen bound of a new test for independence in high dimension with an application to stochastic optimization
- Optimal hypothesis testing for high dimensional covariance matrices
- Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors
- A consistent test of independence based on a sign covariance related to Kendall's tau
- Fast algorithms for the calculation of Kendall's \(\tau\)
- On some test criteria for covariance matrix
- Ordinal Measures of Association
- Approximation Theorems of Mathematical Statistics
- Likelihood Ratio Tests for High‐Dimensional Normal Distributions
- Testing for complete independence in high dimensions
- Asymptotic distribution of the largest off-diagonal entry of correlation matrices
- Asymptotic Statistics
- Tests for High-Dimensional Covariance Matrices
- Berry-Esseen Inequality for Unbounded Exchangeable Pairs
- The distribution of a statistic used for testing sphericity of normal distributions
- A Class of Statistics with Asymptotically Normal Distribution
- A Non-Parametric Test of Independence
- Necessary and sufficient conditions for the asymptotic distribution of the largest entry of a sample correlation matrix
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