An Application of Latent Class Random Coefficient Regression

Lars Erichsen, Per B. Brockhoff

Research output: Journal Article or Conference Article in JournalJournal articleResearchpeer-review

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 or 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.
Original languageEnglish
JournalJournal of Applied Mathematics and Decision Sciences
Volume8
Issue number4
Pages (from-to)201-214
ISSN1173-9126
Publication statusPublished - 2004
Externally publishedYes

Keywords

  • Latent class regression
  • Random coefficient regression
  • Principal component regression
  • finite mixture distributions
  • bootstrapping
  • maximum likelihood estimation
  • EM-algorithm
  • Sensory analysis
  • Consumer preferences
  • Preference mapping

Fingerprint

Dive into the research topics of 'An Application of Latent Class Random Coefficient Regression'. Together they form a unique fingerprint.

Cite this