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Article

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Title

Testing covariance structures belonging to a quadratic subspace under a doubly multivariate model

Authors

[ 1 ] Instytut Matematyki, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[7.4] Mathematics

Year of publication

2024

Published in

Test

Journal year: 2024 | Journal volume: vol. 33 | Journal number: iss. 3

Article type

scientific article

Publication language

english

Keywords
EN
  • doubly multivariate model
  • covariance structure
  • quadratic subspace
  • Rao score test
  • likelihood ratio test
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.

Pages (from - to)

847 - 876

DOI

10.1007/s11749-024-00922-0

URL

https://link.springer.com/article/10.1007/s11749-024-00922-0

License type

CC BY (attribution alone)

Open Access Mode

czasopismo hybrydowe

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Full text of article

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Access level to full text

public

Ministry points / journal

100

Impact Factor

1,2 [List 2023]

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