  Associated kernels have been introduced to improve the classical continuous
kernels for smoothing any functional on several kinds of supports such as
bounded continuous and discrete sets. This work deals with the effects of
combined associated kernels on nonparametric multiple regression functions.
Using the Nadaraya-Watson estimator with optimal bandwidth matrices selected by
cross-validation procedure, different behaviours of multiple regression
estimations are pointed out according the type of multivariate associated
kernels with correlation or not. Through simulation studies, there are no
effect of correlation structures for the continuous regression functions and
also for the associated continuous kernels; however, there exist really effects
of the choice of multivariate associated kernels following the support of the
multiple regression functions bounded continuous or discrete. Applications are
made on two real datasets.
