This paper addresses model selection using information criteria for bi-nary 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.
CITATION STYLE
Dias, J. G. (2006). Model Selection for the Binary Latent Class Model: A Monte Carlo Simulation. In Data Science and Classification (pp. 91–99). Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-34416-0_11
Mendeley helps you to discover research relevant for your work.