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.

翻译：在Selk 和 Gertheis (2022年) 中,对具有多重功能和绝对共变数的模型采用了一种非参数预测方法,从属变量可以是绝对的(二进制或多级)或连续的,因此既考虑分类问题,也考虑回归问题。在本文中,开发了这种方法的无症状特性。给出了回归/分类估计值的统一趋同率。此外,还表明,数据驱动的最小方形交叉校验方法可以自动消除无关的噪音变量。