High-dimensional Edgeworth expansion of the determinant of sample correlation matrix and its error bound

DOI10.1080/17442508.2020.1744604zbMath1490.60059OpenAlexW3013670052MaRDI QIDQ5086635

Gaoming Sun, Junshan Xie

Publication date: 6 July 2022

Published in: Stochastics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/17442508.2020.1744604

zbMATH Keywords

error boundEdgeworth expansionhigh-dimensional approximationsample correlation matrix

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Central limit and other weak theorems (60F05)

Cites Work

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• Central limit theorems for classical likelihood ratio tests for high-dimensional normal distributions
• High-dimensional Edgeworth expansion of a test statistic on independence and its error bound
• High-dimensional asymptotic expansion of LR statistic for testing intraclass correlation structure and its error bound
• Error bounds for asymptotic approximations of the linear discriminant function when the sample sizes and dimensionality are large
• Determinant of sample correlation matrix with application
• Berry-Esseen bound for high dimensional asymptotic approximation of Wilks' lambda distribution
• Fourier analysis of distribution functions. A mathematical study of the Laplace-Gaussian law
• High-Dimensional Edgeworth Expansion of LR Statistic for Testing Circular Symmetric Covariance Structure and Its Error Bound
• Likelihood Ratio Tests for High‐Dimensional Normal Distributions
• Distribution of the determinant of the sample correlation matrix from a mixture normal model
• Multivariate Statistics

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