  In Selk and Gertheiss (2022) a nonparametric prediction method for models
with multiple functional and categorical covariates is introduced. The
dependent variable can be categorical (binary or multi-class) or continuous,
thus both classification and regression problems are considered. In the paper
at hand the asymptotic properties of this method are developed. A uniform rate
of convergence for the regression / classification estimator is given. Further
it is shown that, asymptotically, a data-driven least squares cross-validation
method can automatically remove irrelevant, noise variables.
