Latent class models for classification

Authors:
Jeroen K. Vermunt;Jay Magidson
Affiliations:
Department of Methodology and Statistics, Tilburg University, 5000 LE Tilburg, Netherlands;Statistical Innovations Inc., Belmont, MA
Venue:
Computational Statistics & Data Analysis
Year:
2003

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Abstract

An overview is provided of recent developments in the use of latent class (LC) and other types of finite mixture models for classification purposes. Several extensions of existing models are presented. Two basic types of LC models for classification are defined: supervised and unsupervised structures. Their most important special cases are presented and illustrated with an empirical example.