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dc.contributor.authorHelske, Satu
dc.contributor.authorHelske, Jouni
dc.contributor.authorEerola, Mervi
dc.contributor.editorRitschard, Gilbert
dc.contributor.editorStuder, Matthias
dc.date.accessioned2018-10-30T12:37:20Z
dc.date.available2018-10-30T12:37:20Z
dc.date.issued2018
dc.identifier.citationHelske, S., Helske, J., & Eerola, M. (2018). Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data. In G. Ritschard, & M. Studer (Eds.), <i>Sequence Analysis and Related Approaches : Innovative Methods and Applications</i> (pp. 185-200). Springer. Life Course Research and Social Policies, 10. <a href="https://doi.org/10.1007/978-3-319-95420-2_11" target="_blank">https://doi.org/10.1007/978-3-319-95420-2_11</a>
dc.identifier.otherCONVID_28670145
dc.identifier.otherTUTKAID_79202
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/60046
dc.description.abstractLife course data often consists of multiple parallel sequences, one for each life domain of interest. Multichannel sequence analysis has been used for computing pairwise dissimilarities and finding clusters in this type of multichannel (or multidimensional) sequence data. Describing and visualizing such data is, however, often challenging. We propose an approach for compressing, interpreting, and visualizing the information within multichannel sequences by finding (1) groups of similar trajectories and (2) similar phases within trajectories belonging to the same group. For these tasks we combine multichannel sequence analysis and hidden Markov modelling. We illustrate this approach with an empirical application to life course data but the proposed approach can be useful in various longitudinal problems.fi
dc.format.extent297
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofSequence Analysis and Related Approaches : Innovative Methods and Applications
dc.relation.ispartofseriesLife Course Research and Social Policies
dc.rightsCC BY 4.0
dc.subject.otherlife sequence data
dc.subject.otherlife course
dc.titleCombining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data
dc.typebookPart
dc.identifier.urnURN:NBN:fi:jyu-201810224485
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.contributor.oppiaineTilastotiedefi
dc.contributor.oppiaineStatisticsen
dc.type.urihttp://purl.org/eprint/type/BookItem
dc.date.updated2018-10-22T15:15:05Z
dc.relation.isbn978-3-319-95419-6
dc.description.reviewstatuspeerReviewed
dc.format.pagerange185-200
dc.relation.issn2211-7776
dc.relation.numberinseries10
dc.type.versionpublishedVersion
dc.rights.copyright© 2018 the Authors
dc.rights.accesslevelopenAccessfi
dc.subject.ysoväestötilastot
dc.subject.ysoelämänkaari
dc.subject.ysopitkittäistutkimus
dc.subject.ysosekvensointi
dc.subject.ysoMarkovin ketjut
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p6724
jyx.subject.urihttp://www.yso.fi/onto/yso/p3313
jyx.subject.urihttp://www.yso.fi/onto/yso/p14610
jyx.subject.urihttp://www.yso.fi/onto/yso/p25917
jyx.subject.urihttp://www.yso.fi/onto/yso/p13075
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1007/978-3-319-95420-2_11


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