Advances in Decision Sciences
Volume 8 (2004), Issue 4, Pages 247-260
doi:10.1155/S1173912604000161
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
Abstract
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.