A study of two high-dimensional likelihood ratio tests under alternative hypotheses

DOI10.1142/S2010326317500162zbMath1485.62070OpenAlexW2768133472MaRDI QIDQ4603582

Tiefeng Jiang, Huijun Chen

Publication date: 16 February 2018

Published in: Random Matrices: Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s2010326317500162

zbMATH Keywords

likelihood ratio testcentral limit theoremmultivariate gamma functionhigh-dimensional datamultivariate normal distributionalternative hypothesis

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Hypothesis testing in multivariate analysis (62H15)

Related Items (11)

Testing high dimensional covariance matrices via posterior Bayes factor ⋮ The moderate deviation principles of likelihood ratio tests under alternative hypothesis ⋮ Estimation and testing for panel data partially linear single-index models with errors correlated in space and time ⋮ Multiple change-points detection in high dimension ⋮ Contiguity under high-dimensional Gaussianity with applications to covariance testing ⋮ On LR simultaneous test of high-dimensional mean vector and covariance matrix under non-normality ⋮ Modified Pillai's trace statistics for two high-dimensional sample covariance matrices ⋮ Tests for high-dimensional covariance matrices ⋮ Global one-sample tests for high-dimensional covariance matrices ⋮ Test on the linear combinations of covariance matrices in high-dimensional data ⋮ Moderate deviation principle for likelihood ratio test in multivariate linear regression model

Cites Work

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• A test for the equality of covariance matrices when the dimension is large relative to the sample sizes
• Central limit theorems for classical likelihood ratio tests for high-dimensional normal distributions
• Likelihood ratio tests for covariance matrices of high-dimensional normal distributions
• Corrections to LRT on large-dimensional covariance matrix by RMT
• Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
• Substitution principle for CLT of linear spectral statistics of high-dimensional sample covariance matrices with applications to hypothesis testing
• Optimal hypothesis testing for high dimensional covariance matrices
• Signal detection in high dimension: the multispiked case
• Likelihood Ratio Tests for High‐Dimensional Normal Distributions
• Testing for complete independence in high dimensions
• Tests for High-Dimensional Covariance Matrices
• Some tests for the equality of covariance matrices

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