High-dimensional asymptotic expansion of LR statistic for testing intraclass correlation structure and its error bound

DOI10.1016/j.jmva.2009.05.006zbMath1177.62018OpenAlexW1992880410MaRDI QIDQ1041067

Takayuki Yamada, Yasunori Fujikoshi, Naohiro Kato

Publication date: 27 November 2009

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2009.05.006

zbMATH Keywords

error boundlikelihood ratio statistichigh-dimensional approximationintraclass correlation structure

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Asymptotic distribution theory in statistics (62E20) Hypothesis testing in multivariate analysis (62H15) Measures of association (correlation, canonical correlation, etc.) (62H20) Probabilistic models, generic numerical methods in probability and statistics (65C20)

Related Items (9)

A test for block circular symmetric covariance structure with divergent dimension ⋮ High-Dimensional Edgeworth Expansion of LR Statistic for Testing Circular Symmetric Covariance Structure and Its Error Bound ⋮ High-dimensional Edgeworth expansion of the determinant of sample correlation matrix and its error bound ⋮ High-dimensional Edgeworth expansion of LR statistic for testing block circular symmetry covariance structure and its errors ⋮ High-dimensional asymptotic expansion of the null distribution for Schott’s test statistic for complete independence of normal random variables ⋮ Hypothesis testing on compound symmetric structure of high-dimensional covariance matrix ⋮ Hypothesis testing on linear structures of high-dimensional covariance matrix ⋮ Asymptotic normality and moderate deviation principle for high-dimensional likelihood ratio statistic on block compound symmetry covariance structure ⋮ Approximate normality in testing an exchangeable covariance structure under large- and high-dimensional settings

Cites Work

• On the distribution of the largest eigenvalue in principal components analysis
• Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
• Asymptotic distribution of the LR statistic for equality of the smallest eigenvalues in high-dimensional principal component analysis
• Berry-Esseen bound for high dimensional asymptotic approximation of Wilks' lambda distribution
• Asymptotic Expansion of the Null Distribution of LR Statistic for Multivariate Linear Hypothesis when the Dimension is Large
• Testing for complete independence in high dimensions
• A Dimensional CLT for Non-Central Wilks' Lambda in Multivariate Analysis
• Multivariate Theory for Analyzing High Dimensional Data
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