Functional Networks for Classification and Regression Problems

In this paper we will use functional networks to model some linear and non linear relations among variables. In particular, our method allows us to discover adequate transformations of the response and/or the explanatory variables in multiple linear regression. If we apply this method to a heteroscedastic linear problem, we can estimate all the parameters involved in the model. Furthermore, we will tackle the estimation of classification functions. The proposed approach is compared with other statistical methods for classification and regression problems. Finally, the performance of the proposed procedure is illustrated by a simulation study and by real-life data sets.