Abstract
A hierarchical extension of the finite mixture model is presented that can be used for the analysis of nested data structures. The model permits a simultaneous model-based clustering of lower- and higher-level units. Lower-level observations within higher-level units are assumed to be mutually independent given cluster membership of the higher-level units. The proposed model can be seen as a finite mixture model in which the prior class membership probabilities are assumed to be random, which makes it very similar to the grade-of-membership (GoM) model. The new model is illustrated with an example from organizational psychology.
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Vermunt, J.K., Magidson, J. (2005). Hierarchical Mixture Models for Nested Data Structures. In: Weihs, C., Gaul, W. (eds) Classification — the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28084-7_26
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DOI: https://doi.org/10.1007/3-540-28084-7_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25677-9
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