Abstract
This paper discusses model selection for latent class (LC) models. A large experimental design is set that allows the comparison of the performance of different information criteria for these models, some compared for the first time. Furthermore, the level of separation of latent classes is controlled using a new procedure. The results show that AIC3 (Akaike information criterion with 3 as penalizing factor) outperforms other model selection criteria for LC models.
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Dias, J.G. (2006). Latent Class Analysis and Model Selection. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_10
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DOI: https://doi.org/10.1007/3-540-31314-1_10
Publisher Name: Springer, Berlin, Heidelberg
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