liu.seSearch for publications in DiVA

CiteExport$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_upper_j_idt828",{id:"formSmash:upper:j_idt828",widgetVar:"widget_formSmash_upper_j_idt828",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:upper:exportLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_upper_j_idt829_j_idt831",{id:"formSmash:upper:j_idt829:j_idt831",widgetVar:"widget_formSmash_upper_j_idt829_j_idt831",target:"formSmash:upper:j_idt829:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});

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});
function selectAll()
{
var panelSome = $(PrimeFaces.escapeClientId("formSmash:some"));
var panelAll = $(PrimeFaces.escapeClientId("formSmash:all"));
panelAll.toggle();
toggleList(panelSome.get(0).childNodes, panelAll);
toggleList(panelAll.get(0).childNodes, panelAll);
}
/*Toggling the list of authorPanel nodes according to the toggling of the closeable second panel */
function toggleList(childList, panel)
{
var panelWasOpen = (panel.get(0).style.display == 'none');
// console.log('panel was open ' + panelWasOpen);
for (var c = 0; c < childList.length; c++) {
if (childList[c].classList.contains('authorPanel')) {
clickNode(panelWasOpen, childList[c]);
}
}
}
/*nodes have styleClass ui-corner-top if they are expanded and ui-corner-all if they are collapsed */
function clickNode(collapse, child)
{
if (collapse && child.classList.contains('ui-corner-top')) {
// console.log('collapse');
child.click();
}
if (!collapse && child.classList.contains('ui-corner-all')) {
// console.log('expand');
child.click();
}
}
2012 (English)In: Acta et Commentationes Universitatis Tartuensis de Mathematica, ISSN 1406-2283, E-ISSN 2228-4699, Vol. 16, no 1, p. 13-32Article in journal (Refereed) Published
##### Abstract [en]

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

2012. Vol. 16, no 1, p. 13-32
##### Keywords [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, id: diva2:469199
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt1118",{id:"formSmash:j_idt1118",widgetVar:"widget_formSmash_j_idt1118",multiple:true});
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt1124",{id:"formSmash:j_idt1124",widgetVar:"widget_formSmash_j_idt1124",multiple:true});
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt1130",{id:"formSmash:j_idt1130",widgetVar:"widget_formSmash_j_idt1130",multiple:true}); Available from: 2011-12-22 Created: 2011-12-22 Last updated: 2017-12-08Bibliographically 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_idt1404:0:j_idt1408",widgetVar:"overlay536195",target:"formSmash:j_idt1404:0: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_idt1404:1:j_idt1408",widgetVar:"overlay813054",target:"formSmash:j_idt1404:1:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

urn-nbn$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_j_idt1840",{id:"formSmash:j_idt1840",widgetVar:"widget_formSmash_j_idt1840",showEffect:"fade",hideEffect:"fade",showDelay:500,hideDelay:300,target:"formSmash:altmetricDiv"});});

CiteExport$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_lower_j_idt1893",{id:"formSmash:lower:j_idt1893",widgetVar:"widget_formSmash_lower_j_idt1893",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:lower:exportLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_lower_j_idt1894_j_idt1896",{id:"formSmash:lower:j_idt1894:j_idt1896",widgetVar:"widget_formSmash_lower_j_idt1894_j_idt1896",target:"formSmash:lower:j_idt1894:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});