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Title: Two Monte Carlo studies for latent class segmentation models
Authors: Camilleri, Liberato
Keywords: Expectation-maximization algorithms
Latent structure analysis
Monte Carlo method
Market segmentation
Issue Date: 2007
Publisher: European Technology Institute
Citation: Camilleri, L. (2007). Two Monte Carlo studies for latent class segmentation models. 21st European Simulation and Modelling Conference, St. Julians. 191-198.
Abstract: Model assessment and comparison are essential aspects of statistical inference. The likelihood ratio test is one of the main instruments for model selection; however, this is not appropriate when the model under consideration contains random effects. In this paper, we present two simulation studies for latent class segmentation models. The first Monte Carlo study compares the performance of seven Information Criteria in predicting the correct number of segments. The second study investigates factors that have an effect on segment membership and parameter recovery and affect computational effort.
Appears in Collections:Scholarly Works - FacSciSOR

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