Partially linear functional quantile regression in a reproducing kernel Hilbert space

Yan Zhou; Weiping Zhang; Hongmei Lin; Heng Lian

doi:10.1080/10485252.2022.2073354

Partially linear functional quantile regression in a reproducing kernel Hilbert space

Yan Zhou, Weiping Zhang, Hongmei Lin^*, Heng Lian

^*Corresponding author for this work

Department of Mathematics

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

9 Citations (Scopus)

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.

Original language	English
Pages (from-to)	789–803
Number of pages	15
Journal	Journal of Nonparametric Statistics
Volume	34
Issue number	4
Online published	19 May 2022
DOIs	https://doi.org/10.1080/10485252.2022.2073354
Publication status	Published - Dec 2022

Research Keywords

Convergence rate
prediction risk
quantile regression
rademacher complexity
MODELS
PREDICTION

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10.1080/10485252.2022.2073354

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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.",

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author = "Yan Zhou and Weiping Zhang and Hongmei Lin and Heng Lian",

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T1 - Partially linear functional quantile regression in a reproducing kernel Hilbert space

AU - Zhou, Yan

AU - Zhang, Weiping

AU - Lin, Hongmei

AU - Lian, Heng

PY - 2022/12

Y1 - 2022/12

N2 - 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.

AB - 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.

KW - Convergence rate

KW - prediction risk

KW - quantile regression

KW - rademacher complexity

KW - MODELS

KW - PREDICTION

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DO - 10.1080/10485252.2022.2073354

M3 - RGC 21 - Publication in refereed journal

SN - 1048-5252

VL - 34

SP - 789

EP - 803

JO - Journal of Nonparametric Statistics

JF - Journal of Nonparametric Statistics

IS - 4

ER -

Partially linear functional quantile regression in a reproducing kernel Hilbert space

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Research Keywords

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