Hierarchical Mixture Models for Nested Data Structures

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.

• Type: Conference
• Year: 2004
• Where: GFKL
• Authors: Jeroen K. Vermunt, Jay Magidson
• Keywords: GFKL 2004, Nested Data Structures, Simultaneous Model-based Clustering, finite Mixture Model