Journal of Applied Mathematics and Decision Sciences
Volume 8 (2004), Issue 4, Pages 247-260

An application of latent class random coefficient regression

Lars Erichsen1 and Per Bruun Brockhoff2

1Pharmacokinetics, Novo Nordisk, Novo Nordisk Park G8.2.30, 2670 Maaloev, Denmark
2Informatics and Mathematical Modelling, Richard Petersens Plads, Technichal University of Denmark, Kongens Lyngby DK-2800, Denmark

Copyright © 2004 Lars Erichsen and Per Bruun Brockhoff. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.