Cluster Weighted Modeling with Student-t components

We consider Cluster-Weighted Modeling in order to model functional dependence between some input and output variables based on data coming from an heterogeneous population. While such approach is usually based on Gaussian components, here we extend this framework to Student-t distributions which provide both more realistic tails for real-world data and robust parametric extension to the fitting of data with respect to the alternative Gaussian models. Theoretical results are illustrated on the ground of some numerical simulations.