Panel data analysis: a survey on model-based clustering of time series View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2011-12

AUTHORS

Sylvia Frühwirth-Schnatter

ABSTRACT

Clustering is a widely used statistical tool to determine subsets in a given data set. Frequently used clustering methods are mostly based on distance measures and cannot easily be extended to cluster time series within a panel or a longitudinal data set. The paper reviews recently suggested approaches to model-based clustering of panel or longitudinal data based on finite mixture models. Several approaches are considered that are suitable both for continuous and for categorical time series observations. Bayesian estimation through Markov chain Monte Carlo methods is described in detail and various criteria to select the number of clusters are reviewed. An application to a panel of marijuana use among teenagers serves as an illustration. More... »

PAGES

251-280

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11634-011-0100-0

DOI

http://dx.doi.org/10.1007/s11634-011-0100-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1028590285


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