Testing block-diagonal covariance structure for high-dimensional data under non-normality

DOI10.1016/j.jmva.2016.12.009zbMath1356.62072OpenAlexW2574583617MaRDI QIDQ512027

Takahiro Nishiyama, Yuki Yamada, Masashi Hyodo

Publication date: 23 February 2017

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2016.12.009

zbMATH Keywords

non-normalityhigh dimensionstatistical hypothesis testingtests of covariance structure

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15) Asymptotic properties of parametric tests (62F05)

Related Items (9)

Kronecker delta method for testing independence between two vectors in high-dimension ⋮ Hypothesis Testing for Block-structured Correlation for High Dimensional Variables ⋮ Likelihood ratio tests for many groups in high dimensions ⋮ Block-diagonal test for high-dimensional covariance matrices ⋮ Rank-based indices for testing independence between two high-dimensional vectors ⋮ Canonical correlation coefficients of high-dimensional Gaussian vectors: finite rank case ⋮ Limit theorem associated with Wishart matrices with application to hypothesis testing for common principal components ⋮ Testing for independence of large dimensional vectors ⋮ Likelihood ratio tests under model misspecification in high dimensions

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