Central limit theorem for linear spectral statistics of large dimensional Kendall's rank correlation matrices and its applications

DOI10.1214/20-AOS2013zbMath1475.62168arXiv1912.06357OpenAlexW3189984341MaRDI QIDQ820817

Qinwen Wang, Zeng Li, Run-Ze Li

Publication date: 28 September 2021

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

Full work available at URL: https://arxiv.org/abs/1912.06357

zbMATH Keywords

central limit theoremrandom matrix theorylinear spectral statisticshigh dimensional independent testKendall's rank correlation matrices

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Hypothesis testing in multivariate analysis (62H15) Central limit and other weak theorems (60F05) Measures of association (correlation, canonical correlation, etc.) (62H20) Random matrices (probabilistic aspects) (60B20) Inference from stochastic processes and spectral analysis (62M15) Statistics of extreme values; tail inference (62G32)

Related Items

Limiting spectral distribution of large dimensional Spearman's rank correlation matrices, On eigenvalues of a high-dimensional Kendall's rank correlation matrix with dependence, A CLT for the LSS of large-dimensional sample covariance matrices with diverging spikes, Applications on linear spectral statistics of high-dimensional sample covariance matrix with divergent spectrum

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Central limit theorems for linear spectral statistics of large dimensional \(F\)-matrices
• Tracy-Widom law for the extreme eigenvalues of sample correlation matrices
• Limiting laws of coherence of random matrices with applications to testing covariance structure and construction of compressed sensing matrices
• Edge universality of correlation matrices
• On the convergence of the spectral empirical process of Wigner matrices
• Spectral statistics of large dimensional Spearman's rank correlation matrix and its application
• Central limit theorem for signal-to-interference ratio of reduced rank linear receiver
• Spectral analysis of large dimensional random matrices
• No eigenvalues outside the support of the limiting spectral distribution of large-dimensional sample covariance matrices
• Testing independence in high dimensions with sums of rank correlations
• CLT for linear spectral statistics of large-dimensional sample covariance matrices.
• The asymptotic distributions of the largest entries of sample correlation matrices.
• Testing the independence of sets of large-dimensional variables
• Hypothesis testing on linear structures of high-dimensional covariance matrix
• Tracy-Widom limit for Kendall's tau
• Independence test for high dimensional data based on regularized canonical correlation coefficients
• Substitution principle for CLT of linear spectral statistics of high-dimensional sample covariance matrices with applications to hypothesis testing
• Marčenko-Pastur law for Kendall's tau
• Random matrix theory in statistics: a review
• Random Matrix Methods for Wireless Communications
• Likelihood Ratio Tests for High‐Dimensional Normal Distributions
• Asymptotic distribution of the largest off-diagonal entry of correlation matrices
• Large Sample Covariance Matrices and High-Dimensional Data Analysis
• Distribution-free tests of independence in high dimensions
• High Dimensional Correlation Matrices: The Central Limit Theorem and Its Applications
• Random Matrix Theory and Wireless Communications