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

Yang Guangren; Liu Xiaohui; Lian Heng

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

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

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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.

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《Journal of complexity》 |2021年第10期|101568.1-101568.13|共13页
作者
Yang Guangren; Liu Xiaohui; Lian Heng;
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作者单位

Jinan Univ, Sch Econ, Dept Stat, Guangzhou, Peoples R China;

Jiangxi Univ Finance & Econ, Sch Stat, Nanchang, Jiangxi, Peoples R China;

City Univ Hong Kong, Dept Math, Hong Kong, Peoples R China|City Univ Hong Kong, Shenzhen Res Inst, Shenzhen, Peoples R China;

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正文语种英语
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Functional data; Minimax rate; Quantile regression; Reproducing kernel Hilbert space;

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

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