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Extended GMANOVA Model with a Linearly Structured Covariance MatrixPrimeFaces.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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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2015 (English)Report (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Linköping University Electronic Press, 2015. , p. 17
##### Series

LiTH-MAT-R, ISSN 0348-2960 ; 2015:07
##### Keyword [en]

estimation, extended growth curve model, GMANOVA, linearly structured covariance matrix, residuals
##### National Category

Mathematics
##### Identifiers

URN: urn:nbn:se:liu:diva-117508ISRN: LiTH-MAT-R--2015/07--SEOAI: oai:DiVA.org:liu-117508DiVA, id: diva2:808834
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt432",{id:"formSmash:j_idt432",widgetVar:"widget_formSmash_j_idt432",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt438",{id:"formSmash:j_idt438",widgetVar:"widget_formSmash_j_idt438",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt444",{id:"formSmash:j_idt444",widgetVar:"widget_formSmash_j_idt444",multiple:true});
Available from: 2015-04-29 Created: 2015-04-29 Last updated: 2015-05-21Bibliographically approved
##### In thesis

In this paper we consider the extended generalized multivariate analysis of variance (GMANOVA) with a linearly structured covariance matrix. The main theme is to find explicit estimators for the mean and for the linearly structured covariance matrix. We show how to decompose the residual space, the orthogonal complement to the mean space, into m + 1 orthogonal subspaces and how to derive explicit estimators of the covariance matrix from the sum of squared residuals obtained by projecting observations on those subspaces. Also an explicit estimator of the mean is derived and some properties of the proposed estimators are studied.

1. Bilinear and Trilinear Regression Models with Structured Covariance Matrices$(function(){PrimeFaces.cw("OverlayPanel","overlay813054",{id:"formSmash:j_idt705:0:j_idt709",widgetVar:"overlay813054",target:"formSmash:j_idt705:0:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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CiteExport$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_lower_j_idt1194",{id:"formSmash:lower:j_idt1194",widgetVar:"widget_formSmash_lower_j_idt1194",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:lower:exportLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_lower_j_idt1195_j_idt1197",{id:"formSmash:lower:j_idt1195:j_idt1197",widgetVar:"widget_formSmash_lower_j_idt1195_j_idt1197",target:"formSmash:lower:j_idt1195:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});