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Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data
Linköping University, Department of Management and Engineering, The Institute for Analytical Sociology, IAS. Linköping University, Faculty of Arts and Sciences. Department of Sociology, University of Oxford, Oxford, UK / Department of Mathematics and Statistics, University of Jyvaskyla, Jyvaskyla, Finland.ORCID iD: 0000-0003-0532-0153
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Department of Mathematics and Statistics, University of Jyvaskyla, Jyvaskyla, Finland.ORCID iD: 0000-0001-7130-793X
Centre of Statistics, University of Turku, Turku, Finland.
2018 (English)In: Sequence Analysis and Related Approaches / [ed] Gilbert Ritschard, Matthias Studer, Switzerland: Springer, 2018, p. 185-200Chapter in book (Refereed)
Abstract [en]

Life 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.

Place, publisher, year, edition, pages
Switzerland: Springer, 2018. p. 185-200
Series
Life Course Research and Social Policies, ISSN 2211-7776, E-ISSN 2211-7784 ; 10
Keywords [en]
life course, longitudinal data, sequence analysis, family and work trajectories, Markov models, hidden Markov models, latent Markov models, population dynamics
National Category
Probability Theory and Statistics Social Sciences Interdisciplinary
Identifiers
URN: urn:nbn:se:liu:diva-152155DOI: 10.1007/978-3-319-95420-2_11ISBN: 978-3-319-95420-2 (electronic)ISBN: 978-3-319-95419-6 (print)OAI: oai:DiVA.org:liu-152155DiVA, id: diva2:1257202
Available from: 2018-10-19 Created: 2018-10-19 Last updated: 2018-10-19Bibliographically approved

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Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data(681 kB)41 downloads
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Helske, SatuHelske, Jouni

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