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Estimation of parameters in the extended growth curve 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}); 2012 (English)In: Acta et Commentationes Universitatis Tartuensis de Mathematica, ISSN 1406-2283, Vol. 16, no 1, 13-32 p.Article in journal (Refereed) Published
##### Abstract [en]

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

2012. Vol. 16, no 1, 13-32 p.
##### Keyword [en]

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

Probability Theory and Statistics
##### Identifiers

URN: urn:nbn:se:liu:diva-73218OAI: oai:DiVA.org:liu-73218DiVA: diva2:469199
#####

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Available from: 2011-12-22 Created: 2011-12-22 Last updated: 2015-05-21Bibliographically approved
##### In thesis

In this paper the extended growth curve model with two terms and a linearly structured covariance matrix is considered. We propose an estimation procedure that handles linear structured covariance matrices. The idea is first to estimate the covariance matrix when it should be used to define an inner product in a regression space and thereafter reestimate it when it should be interpreted as a dispersion matrix. This idea is exploited by decomposing the residual space, the orthogonal complement to the design space, into three orthogonal subspaces. Studying residuals obtained from projections of observations on these subspaces yields explicit consistent estimators of the covariance matrix. An explicit consistent estimator of the mean is also proposed and numerical examples are given.

1. Estimation in Multivariate Linear Models with Linearly Structured Covariance Matrices$(function(){PrimeFaces.cw("OverlayPanel","overlay536195",{id:"formSmash:j_idt647:0:j_idt651",widgetVar:"overlay536195",target:"formSmash:j_idt647:0:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. Bilinear and Trilinear Regression Models with Structured Covariance Matrices$(function(){PrimeFaces.cw("OverlayPanel","overlay813054",{id:"formSmash:j_idt647:1:j_idt651",widgetVar:"overlay813054",target:"formSmash:j_idt647:1:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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