Partially linear functional quantile regression in a reproducing kernel Hilbert space

被引：4

作者：

Zhou, Yan ^{[1
]}

Zhang, Weiping ^{[2
]}

Lin, Hongmei ^{[3
]}

Lian, Heng ^{[4
]}

机构：

[1] Shenzhen Univ, Coll Math & Stat, Inst Stat Sci, Shenzhen, Peoples R China

[2] USTC, Dept Stat, Hefei, Peoples R China

[3] Shanghai Univ Int Business & Econ, Sch Stat & Informat, Shanghai, Peoples R China

[4] City Univ Hong Kong, Dept Math, Hong Kong, Peoples R China

来源：

JOURNAL OF NONPARAMETRIC STATISTICS | 2022年 / 34卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Convergence rate; prediction risk; quantile regression; rademacher complexity; MODELS; PREDICTION;

D O I：

10.1080/10485252.2022.2073354

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：789 / 803

页数：15

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