CiteExport$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_upper_j_idt182",{id:"formSmash:upper:j_idt182",widgetVar:"widget_formSmash_upper_j_idt182",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:upper:exportLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_upper_j_idt191_j_idt193",{id:"formSmash:upper:j_idt191:j_idt193",widgetVar:"widget_formSmash_upper_j_idt191_j_idt193",target:"formSmash:upper:j_idt191:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});

On perfect simulation and EM estimationPrimeFaces.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();
}
}
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
##### Abstract [en]

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

Umeå: Print & Media , 2010. , p. 29
##### Series

Doctoral thesis / Umeå University, Department of Mathematics, ISSN 1102-8300
##### Keywords [en]

Perfect simulation, coupling from the past, Markov chain Monte Carlo, point process, Widom-Rowlinson model, EM algorithm, dispersal distribution, fecundity, first-passage percolation
##### National Category

Probability Theory and Statistics
##### Research subject

Mathematical Statistics
##### Identifiers

URN: urn:nbn:se:umu:diva-33779ISBN: 978-91-7264-985-9 (print)OAI: oai:DiVA.org:umu-33779DiVA, id: diva2:318076
##### Public defence

2010-06-01, N420, Umeå universitet, Umeå, 13:01 (English)
##### Opponent

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt732",{id:"formSmash:j_idt732",widgetVar:"widget_formSmash_j_idt732",multiple:true});
##### Supervisors

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt751",{id:"formSmash:j_idt751",widgetVar:"widget_formSmash_j_idt751",multiple:true});
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt776",{id:"formSmash:j_idt776",widgetVar:"widget_formSmash_j_idt776",multiple:true}); Available from: 2010-05-07 Created: 2010-05-06 Last updated: 2018-06-08Bibliographically approved
##### List of papers

Perfect simulation and the EM algorithm are the main topics in this thesis. In paper I, we present coupling from the past (CFTP) algorithms that generate perfectly distributed samples from the multi-type Widom--Rowlin-son (W--R) model and some generalizations of it. The classical W--R model is a point process in the plane or the space consisting of points of several different types. Points of different types are not allowed to be closer than some specified distance, whereas points of the same type can be arbitrary close. A stick-model and soft-core generalizations are also considered. Further, we generate samples without edge effects, and give a bound on sufficiently small intensities (of the points) for the algorithm to terminate.

In paper II, we consider the forestry problem on how to estimate seedling dispersal distributions and effective plant fecundities from spatially data of adult trees and seedlings, when the origin of the seedlings are unknown. Traditional models for fecundities build on allometric assumptions, where the fecundity is related to some characteristic of the adult tree (e.g.\ diameter). However, the allometric assumptions are generally too restrictive and lead to nonrealistic estimates. Therefore we present a new model, the unrestricted fecundity (UF) model, which uses no allometric assumptions. We propose an EM algorithm to estimate the unknown parameters. Evaluations on real and simulated data indicates better performance for the UF model.

In paper III, we propose EM algorithms to estimate the passage time distribution on a graph.Data is obtained by observing a flow only at the nodes -- what happens on the edges is unknown. Therefore the sample of passage times, i.e. the times it takes for the flow to stream between two neighbors, consists of right censored and uncensored observations where it sometimes is unknown which is which. For discrete passage time distributions, we show that the maximum likelihood (ML) estimate is strongly consistent under certain weak conditions. We also show that our propsed EM algorithm converges to the ML estimate if the sample size is sufficiently large and the starting value is sufficiently close to the true parameter. In a special case we show that it always converges. In the continuous case, we propose an EM algorithm for fitting phase-type distributions to data.

1. Perfect simulation of some spatial point processes$(function(){PrimeFaces.cw("OverlayPanel","overlay317868",{id:"formSmash:j_idt923:0:j_idt930",widgetVar:"overlay317868",target:"formSmash:j_idt923:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. Inverse modeling for effective dispersal: do we need tree size to estimate fecundity?$(function(){PrimeFaces.cw("OverlayPanel","overlay317873",{id:"formSmash:j_idt923:1:j_idt930",widgetVar:"overlay317873",target:"formSmash:j_idt923:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

3. Estimation of the passage time distribution on a graph via the EM algorithm$(function(){PrimeFaces.cw("OverlayPanel","overlay317878",{id:"formSmash:j_idt923:2:j_idt930",widgetVar:"overlay317878",target:"formSmash:j_idt923:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

isbn
urn-nbn$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_j_idt1968",{id:"formSmash:j_idt1968",widgetVar:"widget_formSmash_j_idt1968",showEffect:"fade",hideEffect:"fade",showDelay:500,hideDelay:300,target:"formSmash:altmetricDiv"});});

CiteExport$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_lower_j_idt2034",{id:"formSmash:lower:j_idt2034",widgetVar:"widget_formSmash_lower_j_idt2034",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:lower:exportLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_lower_j_idt2035_j_idt2040",{id:"formSmash:lower:j_idt2035:j_idt2040",widgetVar:"widget_formSmash_lower_j_idt2035_j_idt2040",target:"formSmash:lower:j_idt2035:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});