Analysing complex life sequence data with hidden Markov modelling.

Autor/inn/en	Helske, Satu; Helske, Jouni; Eerola, Mervi
Titel	Analysing complex life sequence data with hidden Markov modelling.
Quelle	Aus: Ritschard, Gilbert (Hrsg.); Studer, Matthias (Hrsg.): Proceedings of the international conference on sequence analysis and related methods (LaCOSA II). Lausanne, CH (2016) S. 209-240 PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Beigaben	Literaturangaben
Sprache	englisch
Dokumenttyp	online; gedruckt; Sammelwerksbeitrag
ISBN	978-1-86094-345-4; 978-1-86094-952-4
Schlagwörter	Clusteranalyse; Langzeituntersuchung; Datenanalyse; Daten; Mehrdimensionalität; Sequenz
Abstract	When analysing complex sequence data with multiple channels (dimensions) and long observation sequences, describing and visualizing the data can be a challenge. Hidden Markov models (HMMs) and their mixtures (MHMMs) offer a probabilistic model-based framework where the information in such data can be compressed into hidden states (general life stages) and clusters (general patterns in life courses). We studied two different approaches to analysing clustered life sequence data with sequence analysis (SA) and hidden Markov modelling. In the first approach we used SA clusters as fixed and estimated HMMs separately for each group. In the second approach we treated SA clusters as suggestive and used them as a starting point for the estimation of MHMMs. Even though the MHMM approach has advantages, we found it to be unfeasible in this type of complex setting. Instead, using separate HMMs for SA clusters was useful for finding and describing patterns in life courses. (Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2020/2