Uniform convergence rates and automatic variable selection in nonparametric regression with functional and categorical covariates
Statistics Theory
2023-04-04 v2 Statistics Theory
Abstract
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.
Cite
@article{arxiv.2209.15079,
title = {Uniform convergence rates and automatic variable selection in nonparametric regression with functional and categorical covariates},
author = {Leonie Selk},
journal= {arXiv preprint arXiv:2209.15079},
year = {2023}
}