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Estimation in multivariate linear models with Kronecker product and linear structures on the covariance matricesPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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(English)Manuscript (preprint) (Other academic)
##### Abstract [en]

##### Keywords [en]

Growth curve model, maximum likelihood estimators, estimation equations, flip-flop algorithm, Kronecker product structure, linear structure
##### National Category

Probability Theory and Statistics
##### Identifiers

URN: urn:nbn:se:liu:diva-78844ISRN: LiTH-MAT-R-2012/05-SEOAI: oai:DiVA.org:liu-78844DiVA, id: diva2:536189
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PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt504",{id:"formSmash:j_idt504",widgetVar:"widget_formSmash_j_idt504",multiple:true}); Available from: 2012-06-21 Created: 2012-06-21 Last updated: 2014-09-29Bibliographically approved
##### In thesis

This paper deals with models based on normally distributed random matrices. More specifically the model considered is X ∼ N_{p,q}(M, Σ, Ψ) with mean M, a p×q matrix, assumed to follow a bilinear structure, i.e., *E*[X] = M = ABC, where A and C are known design matrices, B is unkown parameter matrix, and the dispersion matrix of X has a Kronecker product structure, i.e., *D*[X] = Ψ ⊗ Σ, where both Ψ and Σ are unknown positive definite matrices. The model may be used for example to model data with spatiotemporal relationships. The aim is to estimate the parameters of the model when, in addition, Σ is assumed to be linearly structured. In the paper, on the basis of *n* independent observations on the random matrix X, estimation equations in a flip-flop relation are presented and numerical examples are given.

1. Estimation in Multivariate Linear Models with Linearly Structured Covariance Matrices$(function(){PrimeFaces.cw("OverlayPanel","overlay536195",{id:"formSmash:j_idt783:0:j_idt787",widgetVar:"overlay536195",target:"formSmash:j_idt783:0:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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