Article(electronic)#1December 1, 2004
An application of latent class random coefficient regression
In: Journal of applied mathematics & decision sciences: JAMDS, Volume 8, Issue 4, p. 247-260
ISSN: 1532-7612
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.