Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/19187
Title: Latent class mixture models for analyzing rating responses
Authors: Camilleri, Liberato
Keywords: Expectation-maximization algorithms
Monte Carlo method
Latent structure analysis
Issue Date: 2009
Publisher: European Technology Institute
Citation: Camilleri, L. (2009). Latent class mixture models for analyzing rating responses. 7th Annual Industrial Simulation Conference, Loughborough. 1-5.
Abstract: Latent class methodology has been used extensively in market research. In this approach, segment membership and parameter estimates for each derived segment are estimated simultaneously. A popular approach for fitting latent class models to rating responses is to assume mixtures of multivariate conditional normal distributions. An alternative approach is to assume a Proportional Odds model. These two approaches are compared empirically in a Monte Carlo study, assessing segment membership and parameter recovery, goodness of fit and predictive accuracy.
URI: https://www.um.edu.mt/library/oar//handle/123456789/19187
Appears in Collections:Scholarly Works - FacSciSOR

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