  We consider nonparametric prediction with multiple covariates, in particular
categorical or functional predictors, or a mixture of both. The method proposed
bases on an extension of the Nadaraya-Watson estimator where a kernel function
is applied on a linear combination of distance measures each calculated on
single covariates, with weights being estimated from the training data. The
dependent variable can be categorical (binary or multi-class) or continuous,
thus we consider both classification and regression problems. The methodology
presented is illustrated and evaluated on artificial and real world data.
Particularly it is observed that prediction accuracy can be increased, and
irrelevant, noise variables can be identified/removed by `downgrading' the
corresponding distance measures in a completely data-driven way.
