Hypothesis testing for independence under blocked compound symmetric covariance structure

DOI10.1007/s40304-018-0130-4zbMath1392.62165OpenAlexW2800074833WikidataQ129969229 ScholiaQ129969229MaRDI QIDQ722082

Shin-ichi Tsukada

Publication date: 20 July 2018

Published in: Communications in Mathematics and Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s40304-018-0130-4

zbMATH Keywords

independenceasymptotic distributionhypothesis testingblocked compound symmetric covariance structure

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Hypothesis testing in multivariate analysis (62H15) Exact distribution theory in statistics (62E15)

Related Items (6)

Doubly multivariate linear models with block exchangeable distributed errors and site-dependent covariates ⋮ Linear models for multivariate repeated measures data with block exchangeable covariance structure ⋮ Hypothesis testing for independence given a blocked compound symmetric covariance structure in a high-dimensional setting ⋮ Testing independence under a block compound symmetry covariance structure ⋮ Score tests for intercept and slope parameters of doubly multivariate linear models with skew-normal errors ⋮ Asymptotic distribution of correlation matrix under blocked compound symmetric covariance structure

Cites Work

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• Linear discrimination with equicorrelated training vectors
• Optimal estimation for doubly multivariate data in blocked compound symmetric covariance structure
• Testing of multivariate repeated measures data with block exchangeable covariance structure
• Testing the hypothesis of a block compound symmetric covariance matrix for elliptically contoured distributions
• Testing the equality of mean vectors for paired doubly multivariate observations in blocked compound symmetric covariance matrix setup
• Estimating and Testing a Structured Covariance Matrix for Three-Level Multivariate Data
• Linear Models with Exchangeably Distributed Errors
• High‐Dimensional Covariance Estimation
• Multivariate Statistics

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