函数协变量和分类协变量非参数回归的一致收敛率和自动变量选择

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY Journal of Nonparametric Statistics Pub Date : 2022-09-29 DOI:10.1080/10485252.2023.2207673

Leonie Selk

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引用次数: 0

摘要

在Selk和Gertheiss(2022)中，介绍了具有多个函数和分类协变量的模型的非参数预测方法。因变量可以是分类的(二元或多类)或连续的，因此分类和回归问题都被考虑。本文给出了该方法的渐近性质。给出了回归/分类估计器的统一收敛速率。进一步表明，渐近地，数据驱动的最小二乘交叉验证方法可以自动去除不相关的噪声变量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Uniform convergence rates and automatic variable selection in nonparametric regression with functional and categorical covariates

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.

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来源期刊

Journal of Nonparametric Statistics 数学-统计学与概率论

CiteScore

1.50

自引率

8.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Journal of Nonparametric Statistics provides a medium for the publication of research and survey work in nonparametric statistics and related areas. The scope includes, but is not limited to the following topics: Nonparametric modeling, Nonparametric function estimation, Rank and other robust and distribution-free procedures, Resampling methods, Lack-of-fit testing, Multivariate analysis, Inference with high-dimensional data, Dimension reduction and variable selection, Methods for errors in variables, missing, censored, and other incomplete data structures, Inference of stochastic processes, Sample surveys, Time series analysis, Longitudinal and functional data analysis, Nonparametric Bayes methods and decision procedures, Semiparametric models and procedures, Statistical methods for imaging and tomography, Statistical inverse problems, Financial statistics and econometrics, Bioinformatics and comparative genomics, Statistical algorithms and machine learning. Both the theory and applications of nonparametric statistics are covered in the journal. Research applying nonparametric methods to medicine, engineering, technology, science and humanities is welcomed, provided the novelty and quality level are of the highest order. Authors are encouraged to submit supplementary technical arguments, computer code, data analysed in the paper or any additional information for online publication along with the published paper.