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Partially linear functional quantile regression in a reproducing kernel Hilbert space

Author

Listed:
  • Yan Zhou
  • Weiping Zhang
  • Hongmei Lin
  • Heng Lian

Abstract

We consider quantile functional regression with a functional part and a scalar linear part. We establish the optimal prediction rate for the model under mild assumptions in the reproducing kernel Hilbert space (RKHS) framework. Under stronger assumptions related to the capacity of the RKHS, the non-functional linear part is shown to have asymptotic normality. The estimators are illustrated in simulation studies.

Suggested Citation

  • Yan Zhou & Weiping Zhang & Hongmei Lin & Heng Lian, 2022. "Partially linear functional quantile regression in a reproducing kernel Hilbert space," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 34(4), pages 789-803, October.
    • Handle: RePEc:taf:gnstxx:v:34:y:2022:i:4:p:789-803
    DOI: 10.1080/10485252.2022.2073354
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