论文标题
非参数回归中的均匀收敛速率和自动变量选择，具有功能和分类协变量
Uniform convergence rates and automatic variable selection in nonparametric regression with functional and categorical covariates
论文作者
论文摘要
在Selk和Gertheiss（2022）中，引入了具有多个功能和分类协变量模型的非参数预测方法。因变量可以分类（二进制或多类）或连续，因此考虑了分类和回归问题。在手头的论文中，该方法的渐近特性得到了开发。给出了回归 /分类估计器的均匀收敛速率。此外，渐近地表明，数据驱动的最小二乘跨验证方法可以自动消除无关的噪声变量。
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.
