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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.
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: 2022-03-15Bibliographically approved

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Liang, Yuli

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