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Functional single-index composite quantile regression

Author

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  • Zhiqiang Jiang

    (Anhui Polytechnic University
    Nanjing University of Science and Technology)

  • Zhensheng Huang

    (Nanjing University of Science and Technology)

  • Jing Zhang

    (Nanjing University of Science and Technology)

Abstract

The functional single-index model is a very flexible semiparametric model when modeling the relationship between a scalar response and functional predictors. However, the efficiency of the model may be affected by non-normal errors. So, in this paper, we propose functional single index composite quantile regression. The unknown slope function and link function are estimated by using B-spline basis functions. The convergence rates of the estimators are established. Some simulation studies and an application of NIR spectroscopy dataset are presented to illustrate the performance of the proposed methodologies.

Suggested Citation

  • Zhiqiang Jiang & Zhensheng Huang & Jing Zhang, 2023. "Functional single-index composite quantile regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(5), pages 595-603, July.
    • Handle: RePEc:spr:metrik:v:86:y:2023:i:5:d:10.1007_s00184-022-00887-w
    DOI: 10.1007/s00184-022-00887-w
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    References listed on IDEAS

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    Cited by:

    1. Salim Bouzebda, 2024. "Limit Theorems in the Nonparametric Conditional Single-Index U -Processes for Locally Stationary Functional Random Fields under Stochastic Sampling Design," Mathematics, MDPI, vol. 12(13), pages 1-81, June.

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