An Application of Latent Class Random Coefficient Regression

In this paper we apply a statistical model combining a random coefficient regression model and a latent class regression model. The EM-algorithm is used for maximum likelihood estimation of the unknown parameters in the model and it is pointed out how this leads to a straightforward handling of a number of different variance/covariance restrictions.

Finally, the model is used to analyze how consumers' preferences for eight coffee samples relate to sensory characteristics of the coffees. Within this application the analysis corresponds to a model-based version of the so-called external preference mapping.