Partially linear functional quantile regression in a reproducing kernel Hilbert space

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

4 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)789–803
Number of pages15
Journal / PublicationJournal of Nonparametric Statistics
Volume34
Issue number4
Online published19 May 2022
Publication statusPublished - Dec 2022

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.

Research Area(s)

  • Convergence rate, prediction risk, quantile regression, rademacher complexity, MODELS, PREDICTION

Citation Format(s)

Partially linear functional quantile regression in a reproducing kernel Hilbert space. / Zhou, Yan; Zhang, Weiping; Lin, Hongmei et al.
In: Journal of Nonparametric Statistics, Vol. 34, No. 4, 12.2022, p. 789–803.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review