INFERENCE BASED ON RESAMPLING TECHNIQUES FOR NEURAL NETWORKS IN REGRESSION MODELS

Let {Yt}, t=1,..., T be a time series generated according to the model: Yt=f(Xt)+et t=1, ..., T where f is a non linear continuous function, Xt = (X1t, X2t, ...,Xdt) is a vector of d non stochastic explanatory variables defined on a compact X belonging Rd , and {et} are zero mean random variables with constant variance. The function f in the previous model can be approximated with a feed-forward neural networks. Many authors (Hornik et al., 1989 inter alia) showed that, under general regularity conditions, a sufficiently complex single hidden layer feedforward network can approximate any member of a class of function to any degree of accuracy. Of course to consider them a statistical techni…