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Testing covariance structures belonging to a quadratic subspace under a doubly multivariate model
Poznan University of Technology, Poland.ORCID iD: 0000-0002-6208-8322
Poznan University of Technology, Poland.ORCID iD: 0000-0001-6780-7800
Linnaeus University, School of Business and Economics, Department of Economics and Statistics (NS). (DISA)
2024 (English)In: Test (Madrid), ISSN 1133-0686, E-ISSN 1863-8260, Vol. 33, p. 847-876Article in journal (Refereed) Published
Abstract [en]

A hypothesis related to the block structure of a covariance matrix under the doubly multivariate normal model is studied. It is assumed that the block structure of the covariance matrix belongs to a quadratic subspace, and under the null hypothesis, each block of the covariance matrix also has a structure belonging to some quadratic subspace. The Rao score and the likelihood ratio test statistics are derived, and the exact distribution of the likelihood ratio test is determined. Simulation studies show the advantage of the Rao score test over the likelihood ratio test in terms of speed of convergence to the limiting chi-square distribution, while both proposed tests are competitive in terms of their power. The results are applied to both simulated and real-life example data.

Place, publisher, year, edition, pages
Springer, 2024. Vol. 33, p. 847-876
Keywords [en]
Doubly multivariate model, Covariance structure, Quadratic subspace, Rao score test, Likelihood ratio test
National Category
Probability Theory and Statistics
Research subject
Mathematics, Applied Mathematics
Identifiers
URN: urn:nbn:se:lnu:diva-128007DOI: 10.1007/s11749-024-00922-0ISI: 001172429600001Scopus ID: 2-s2.0-85186234008OAI: oai:DiVA.org:lnu-128007DiVA, id: diva2:1840535
Available from: 2024-02-25 Created: 2024-02-25 Last updated: 2024-12-18Bibliographically approved

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Liang, Yuli

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