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

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  • Xu Qin

    (University of Electronic Science and Technology of China)

  • Huiqun Gao

    (University of Electronic Science and Technology of China)

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.

Suggested Citation

  • Xu Qin & Huiqun Gao, 2024. "Nonparametric binary regression models with spherical predictors based on the random forests kernel," Computational Statistics, Springer, vol. 39(6), pages 3031-3048, September.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:6:d:10.1007_s00180-023-01422-9
    DOI: 10.1007/s00180-023-01422-9
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    References listed on IDEAS

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    1. Arthur Pewsey & Eduardo García-Portugués, 2021. "Rejoinder on: Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 76-82, March.
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