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• W2897813905 abstract "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." @default.
• W2897813905 created "2018-10-26" @default.
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• W2897813905 date "2018-01-01" @default.
• W2897813905 modified "2023-09-24" @default.
• W2897813905 title "Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data" @default.
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• W2897813905 doi "https://doi.org/10.1007/978-3-319-95420-2_11" @default.
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