参数帮助下的希尔伯特加性回归

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Nonparametric Statistics Pub Date : 2023-02-28 DOI:10.1080/10485252.2023.2182153

Young K. Lee, E. Mammen, Byeong-U Park

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引用次数: 0

摘要

讨论了当响应变量在一般可分Hilbert空间中取值时，改进局部线性加性回归的一种方法。我们的方法涵盖了非加性回归函数以及加性回归函数的情况。我们在这个灵活的框架中提出了相关理论，并通过实际数据应用证明了所提出技术的优点。

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Hilbertian additive regression with parametric help

We discuss a way of improving local linear additive regression when the response variable takes values in a general separable Hilbert space. Our methodology covers the case of non-additive regression function as well as additive. We present relevant theory in this flexible framework and demonstrate the benefits of the proposed technique via a real data application.

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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.