Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data
Helske, 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.), Sequence Analysis and Related Approaches : Innovative Methods and Applications (pp. 185-200). Springer. Life Course Research and Social Policies, 10. https://doi.org/10.1007/978-3-319-95420-2_11
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Life Course Research and Social PoliciesDate
2018Copyright
© 2018 the Authors
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.
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SpringerParent publication ISBN
978-3-319-95419-6Is part of publication
Sequence Analysis and Related Approaches : Innovative Methods and ApplicationsISSN Search the Publication Forum
2211-7776Keywords
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https://converis.jyu.fi/converis/portal/detail/Publication/28670145
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