Robust order selection of mixtures of regression models with random effects

Finite mixtures of regression models with random effects are a very flexible statistical tool to model data, as these allow the heterogeneity population and account for multiple correlated observations from same individual at time. The selection number components has been long-standing challenging problem in statistics. However, majority existent methods estimation not robust and, therefore, quite sensitive outliers. In this article we study effects, investigating performance trimmed information classification criteria comparatively traditional criteria. simulation real-world application showcase superiority presence contaminated data.