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Estimation equations for multivariate linear models with Kronecker structured covariance matrices
Poznan University of Life Science, Poland.
Shanghai University of Int Business and Econ, China.
Statistics Sweden, Sweden.ORCID iD: 0000-0001-6581-7570
Swedish University of Agricultural Science, Sweden;Linköping University, Sweden.
2017 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 46, no 16, p. 7902-7915Article in journal (Refereed) Published
Abstract [en]

The aim of the paper is to determine maximum-likelihood estimation equations. Observations follow a multivariate normal distribution, X-i similar to N-p,N-q (mu, Psi, Sigma), where D[X-i] = Sigma circle times Psi, Psi and Sigma describe the unknown covariance structure between rows and columns of X-i, respectively. Imposing restrictions on Psi and Sigma four types of covariance structures will be considered.

Place, publisher, year, edition, pages
Taylor & Francis, 2017. Vol. 46, no 16, p. 7902-7915
Keywords [en]
Compound symmetric structure; Kronecker product; matrix derivatives; maximum-likelihood estimation
National Category
Probability Theory and Statistics
Research subject
Statistics/Econometrics
Identifiers
URN: urn:nbn:se:lnu:diva-110528DOI: 10.1080/03610926.2016.1165852ISI: 000405910600009Scopus ID: 2-s2.0-85018805881OAI: oai:DiVA.org:lnu-110528DiVA, id: diva2:1639141
Available from: 2022-02-18 Created: 2022-02-18 Last updated: 2025-05-15Bibliographically approved

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Liang, Yuli

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