Spectral statistics of large dimensional Spearman's rank correlation matrix and its application

DOI10.1214/15-AOS1353zbMath1328.15046arXiv1312.5119OpenAlexW1933489904MaRDI QIDQ892251

Zhigang Bao, Liang-Ching Lin, Wang Zhou, Guangming Pan

Publication date: 18 November 2015

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1312.5119

zbMATH Keywords

central limit theoremrandom matrixindependence testlinear spectral statisticsnonparametric methodSpearman's rank correlation matrix

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Random matrices (probabilistic aspects) (60B20) Random matrices (algebraic aspects) (15B52)

Related Items (13)

Central limit theorem for linear spectral statistics of large dimensional Kendall's rank correlation matrices and its applications ⋮ A simpler spatial-sign-based two-sample test for high-dimensional data ⋮ Limiting spectral distribution of large dimensional Spearman's rank correlation matrices ⋮ Spectral statistics of large dimensional Spearman's rank correlation matrix and its application ⋮ The volume of random simplices from elliptical distributions in high dimension ⋮ On eigenvalues of a high-dimensional Kendall's rank correlation matrix with dependence ⋮ Linear eigenvalue statistics of XX′ matrices ⋮ Max-sum test based on Spearman's footrule for high-dimensional independence tests ⋮ Rank-based indices for testing independence between two high-dimensional vectors ⋮ An extreme-value approach for testing the equality of large U-statistic based correlation matrices ⋮ Joint convergence of sample autocovariance matrices when \(p/n\to 0\) with application ⋮ Tracy-Widom limit for Kendall's tau ⋮ Hypothesis tests for high-dimensional covariance structures

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