Central limit theorem for linear spectral statistics of large dimensional separable sample covariance matrices

DOI10.3150/18-BEJ1038zbMath1466.60008OpenAlexW2963403464MaRDI QIDQ2419661

Huiqin Li, Guangming Pan, Zhi-Dong Bai

Publication date: 14 June 2019

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.bj/1560326430

zbMATH Keywords

central limit theoremrandom matrix theorylinear spectral statisticsseparable sample covariance matrix

Mathematics Subject Classification ID

Central limit and other weak theorems (60F05) Random matrices (probabilistic aspects) (60B20)

Related Items

Central limit theorem for linear spectral statistics of general separable sample covariance matrices with applications, Gaussian fluctuations for products of random matrices, Joint CLT for top eigenvalues of sample covariance matrices of separable high dimensional long memory processes, Central limit theorem for linear spectral statistics of large dimensional separable sample covariance matrices, A CLT for the LSS of large-dimensional sample covariance matrices with diverging spikes, A functional CLT for partial traces of random matrices

Cites Work

• Unnamed Item
• Gaussian fluctuations for linear spectral statistics of large random covariance matrices
• Central limit theorems for classical likelihood ratio tests for high-dimensional normal distributions
• Limit of the smallest eigenvalue of a large dimensional sample covariance matrix
• Central limit theorem for signal-to-interference ratio of reduced rank linear receiver
• No eigenvalues outside the support of the limiting empirical spectral distribution of a separable covariance matrix
• On the limit of the largest eigenvalue of the large dimensional sample covariance matrix
• Some limit theorems for the eigenvalues of a sample covariance matrix
• Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
• CLT for linear spectral statistics of large-dimensional sample covariance matrices.
• Distribution of linear statistics of singular values of the product of random matrices
• Central limit theorem for linear spectral statistics of large dimensional separable sample covariance matrices
• Analysis of the limiting spectral measure of large random matrices of the separable covariance type
• Central Limit Theorem for linear eigenvalue statistics of the Wigner and sample covariance random matrices
• On the distribution of SINR for the MMSE MIMO receiver and performance analysis
• Sample Eigenvalue Based Detection of High-Dimensional Signals in White Noise Using Relatively Few Samples
• Large Sample Covariance Matrices and High-Dimensional Data Analysis
• Random Matrix Theory and Wireless Communications