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Nonparametric binary regression models with spherical predictors based on the random forests kernel

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Abstract

Spherical data arise widely in various settings. Spherical statistics is an analysis of data on a unit hyper-spherical domain. In this paper, we mainly consider the local kernel estimators for regression models with a binary response and the predictors including spherical variables. We apply the random forests kernel to nonparametric binary regression models with spherical predictors. Simulation experiments and real examples are used to validate the performance of the new models. Compared with the classical von Mises–Fisher kernel and the linear-spherical kernel, the random forests kernel has better fitting effect and faster computation speed. Compared with other classifiers, the models proposed in this paper have better classification performance in both low and high dimensional cases.

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Acknowledgements

The authors thanks for the two anonymous reviewers with their comments about the work.

Funding

This work is supported by the National Natural Science Foundation of China (Grant No. 72033002) and Distinguished Young Scholars of Sichuan Province (No. 2022JDJQ0035).

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Authors and Affiliations

  1. School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China

    Xu Qin & Huiqun Gao

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  1. Xu Qin
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  2. Huiqun Gao
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Correspondence to Xu Qin.

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Qin, X., Gao, H. Nonparametric binary regression models with spherical predictors based on the random forests kernel. Comput Stat (2023). https://doi.org/10.1007/s00180-023-01422-9

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  • DOI: https://doi.org/10.1007/s00180-023-01422-9

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