Optimal prediction for high-dimensional functional quantile regression in reproducing kernel Hilbert spaces

被引：4

作者：

Yang, Guangren ^{[1
]}

Liu, Xiaohui ^{[2
]}

Lian, Heng ^{[3
,4
]}

机构：

[1] Jinan Univ, Sch Econ, Dept Stat, Guangzhou, Peoples R China

[2] Jiangxi Univ Finance & Econ, Sch Stat, Nanchang, Jiangxi, Peoples R China

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

[4] City Univ Hong Kong, Shenzhen Res Inst, Shenzhen, Peoples R China

来源：

JOURNAL OF COMPLEXITY | 2021年 / 66卷

基金：

中国博士后科学基金;

关键词：

Functional data; Minimax rate; Quantile regression; Reproducing kernel Hilbert space; LINEAR-REGRESSION; OPTIMAL RATES; MODELS; CONVERGENCE; ESTIMATORS; SPARSITY; MINIMAX; SINGLE;

D O I：

10.1016/j.jco.2021.101568

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Regression problems with multiple functional predictors have been studied previously. In this paper, we investigate functional quantile linear regression with multiple functional predictors within the framework of reproducing kernel Hilbert spaces. The estimation procedure is based on an l(1)-mixed-norm penalty. The learning rate of the estimator in prediction loss is established and a lower bound on the learning rate is also presented that matches the upper bound up to a logarithmic term. (C) 2021 Elsevier Inc. All rights reserved.

引用

页数：13

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