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. © Springer-Verlag Berlin, Heidelberg 2005.
CITATION STYLE
Vermunt, J. K., & Magidson, J. (2005). Hierarchical mixture models for nested data structures. In Studies in Classification, Data Analysis, and Knowledge Organization (pp. 240–247). Kluwer Academic Publishers. https://doi.org/10.1007/3-540-28084-7_26
Mendeley helps you to discover research relevant for your work.