6533b854fe1ef96bd12ae0bf
RESEARCH PRODUCT
Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data
Mervi EerolaJouni HelskeSatu Helskesubject
longitudinal datasekvensointisequence analysisSequence analysisComputer scienceMarkovin ketjutMarkov modelspitkittäistutkimuselämänkaari01 natural sciences010104 statistics & probability03 medical and health sciencesData sequencespopulation dynamicsSannolikhetsteori och statistik0101 mathematicsfamily and work trajectoriesProbability Theory and StatisticsHidden Markov modellife course030505 public healthhidden Markov modelslatent Markov modelsbusiness.industryPattern recognitionTvärvetenskapliga studier inom samhällsvetenskaplife sequence dataLife domainLife course approachPairwise comparisonArtificial intelligenceSocial Sciences Interdisciplinary0305 other medical sciencebusinessväestötilastotdescription
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. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-01-01 |