A Review of Recent Developments in Latent Class Regression Models

The development of mixture models can be historically traced back to the work of Newcomb (1886) and Pearson (1894). Mixture distributions have been of considerable interest in recent years leading to a vast number of methodological and applied papers, as well as to three dedicated mono­graphs (cf. Everitt and Hand, 1981; Titterington, Smith, and Makov, 1985; and Mclachlan and Basford, 1988). In finite mixture models, it is assumed that a sample of observations arises from a (initially specified) number of underlying classes of unknown proportions. A concrete form of the density of the observations in each of the underlying classes is specified, and the purpose of the finite mixture approach is to decompose the sample into its mixture components...