Abstract
This paper addresses model selection using information criteria for binary latent class (LC) models. A Monte Carlo study sets an experimental design to compare the performance of different information criteria for this model, 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) has a balanced performance for binary LC models.
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Dias, J.G. (2006). Model Selection for the Binary Latent Class Model: A Monte Carlo Simulation. In: Batagelj, V., Bock, HH., Ferligoj, A., Žiberna, A. (eds) Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-34416-0_11
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DOI: https://doi.org/10.1007/3-540-34416-0_11
Publisher Name: Springer, Berlin, Heidelberg
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