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dc.contributor.authorPenttinen, Antti
dc.contributor.authorYlitalo, Anna-Kaisa
dc.date.accessioned2016-05-31T05:17:29Z
dc.date.available2018-04-20T21:45:08Z
dc.date.issued2016
dc.identifier.citationPenttinen, A., & Ylitalo, A.-K. (2016). Deducing self-interaction in eye movement data using sequential spatial point processes. <i>Spatial Statistics</i>, <i>17</i>, 1-21. <a href="https://doi.org/10.1016/j.spasta.2016.03.005" target="_blank">https://doi.org/10.1016/j.spasta.2016.03.005</a>
dc.identifier.otherCONVID_25665055
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/49995
dc.description.abstractEye movement data are outputs of an analyser tracking the gaze when a person is inspecting a scene. These kind of data are of increasing importance in scientific research as well as in applications, e.g. in marketing and human-computer interface design. Thus the new areas of application call for advanced analysis tools. Our research objective is to suggest statistical modelling of eye movement sequences using sequential spatial point processes, which decomposes the variation in data into structural components having interpretation. We consider three elements of an eye movement sequence: heterogeneity of the target space, contextuality between subsequent movements, and time-dependent behaviour describing self-interaction. We propose two model constructions. One is based on the history-dependent rejection of transitions in a random walk and the other makes use of a history-adapted kernel function penalized by user-defined geometric model characteristics. Both models are inhomogeneous self-interacting random walks. Statistical inference based on the likelihood is suggested, some experiments are carried out, and the models are used for determining the uncertainty of important data summaries for eye movement data.en
dc.languageeng
dc.language.isoeng
dc.publisherElsevier BV
dc.relation.ispartofseriesSpatial Statistics
dc.subject.othercoverage
dc.subject.otherheterogeneous media
dc.subject.otherlikelihood
dc.subject.otherself-interacting random walk
dc.subject.otherstochastic geometry
dc.titleDeducing self-interaction in eye movement data using sequential spatial point processes
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201605302765
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosMusiikin laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.contributor.laitosDepartment of Musicen
dc.contributor.oppiaineTilastotiedefi
dc.contributor.oppiaineMusiikkitiedefi
dc.contributor.oppiaineStatisticsen
dc.contributor.oppiaineMusicologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2016-05-30T12:15:08Z
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1-21
dc.relation.issn2211-6753
dc.relation.numberinseries0
dc.relation.volume17
dc.type.versionacceptedVersion
dc.rights.copyright© 2016 Elsevier B.V. This is a final draft version of an article whose final and definitive form has been published by Elsevier. Published in this repository with the kind permission of the publisher.
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber275929
dc.subject.ysomallintaminen
dc.subject.ysokatseenseuranta
dc.subject.ysosilmänliikkeet
dc.subject.ysokatse
dc.subject.ysotietojärjestelmät
dc.subject.ysostokastiset prosessit
jyx.subject.urihttp://www.yso.fi/onto/yso/p3533
jyx.subject.urihttp://www.yso.fi/onto/yso/p37956
jyx.subject.urihttp://www.yso.fi/onto/yso/p23744
jyx.subject.urihttp://www.yso.fi/onto/yso/p27143
jyx.subject.urihttp://www.yso.fi/onto/yso/p3927
jyx.subject.urihttp://www.yso.fi/onto/yso/p11400
dc.relation.doi10.1016/j.spasta.2016.03.005
dc.relation.funderSuomen Akatemiafi
dc.relation.funderResearch Council of Finlanden
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundinginformationThe second author has been financially supported by the Finnish Doctoral Programme in Stochastic and Statistics and by the Academy of Finland (Project number 275929).
dc.type.okmA1


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