Combining sequence analysis and hidden Markov Models in the analysis of complex life sequence data.

Autor/inn/en	Helske, Satu; Helske, Jouni; Eerola, Mervi
Titel	Combining sequence analysis and hidden Markov Models in the analysis of complex life sequence data.
Quelle	Aus: Ritschard, Gilbert (Hrsg.); Studer, Matthias (Hrsg.): Sequence analysis and related approaches. Innovative methods and applications. 1. Aufl. Cham, Schweiz: Springer Open (2018) S. 185-200 PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Reihe	Life course research and social policies. 10
Beigaben	Literaturangaben
Sprache	englisch
Dokumenttyp	online; gedruckt; Sammelwerksbeitrag
ISBN	978-3-319-95419-6; 978-3-319-95420-2
DOI	10.1007/978-3-319-95420-2_11
Schlagwörter	Datenerhebung; Markowscher Prozess; Analyse; Daten; Sequenz; NEPS (National Educational Panel Study)
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. (Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2021/2