UM TUTORIAL SOBRE ESTIMAÇÃO EM MODELOS DE MISTURA - ZUCARELI, Laisa Ribeiro; SARAIVA, Erlandson Ferreira; SUZUKI, Adriano Kamimura (Brazilian Journal of Biometrics, 2018)
In the last years, there has been a growing interest in modeling using mixture models due its exibility. One of the main issues in the estimation procedure of its parameters is the determination of the number of components. In this paper, through a Monte Carlo simulation study, we compared the performance of the model selection criteria AIC, AICc and BIC for determining the number of components of a mixture model. The results obtained show a complementarity between the criteria and that these should be used with some care, since they present percentages of success less than 70% in most of the studied cases.