  Let Y be a response variable related with a set of explanatory variables and
let f1, f2, ..., fk be a set of the parametric forms representing a set of
candidate's model. Let f* be the true model among the set of k plausible
models. We discuss in this paper the use of wavelet regression method as
auxiliary for the choice of the "true" parametric form of a regression model,
particularly, for the cases of nonlinear regression and generalized linear
models. The use of a non-parametric method for the choice of the more
appropriate parametric equation in regression problems would be interesting in
practice due to the simplicity and because the probabilistic assumptions are
not required. We evaluate the performance of the proposed wavelet procedure
based on the true classification rate of the correct parametric form among a
range of k candidate models, taking into account a wide ranges of scenarios and
configurations as well as in real data set applications.
