Reduced rank modeling for functional regression with functional responses

Lin, Hongmei1; Jiang, Xuejun2; Lian, Heng3; Zhang, Weiping4

2019-01

10.1016/j.jmva.2018.09.004

JOURNAL OF MULTIVARIATE ANALYSIS   影响因子和分区

0047-259X

This article considers regression problems where both the predictor and the response are functional in nature. Driven by the desire to build a parsimonious model, we consider functional reduced rank regression in the framework of reproducing kernel Hilbert spaces, which can be formulated in the form of linear factor regression with estimated multivariate factors, and achieves dimension reduction in both the predictor and the response spaces. The convergence rate of the estimator is derived. Simulations and real datasets are used to demonstrate the competitive performance of the proposed method. (C) 2018 Elsevier Inc. All rights reserved.

Dimension reduction Functional data Functional response Reproducing kernel Hilbert space

SCI

英语

其他

Hong Kong RGC General Research Fund[11301718]

Mathematics

Statistics & Probability

WOS:000453640100013

ELSEVIER INC

MATHEMATICS

Web of Science

1.Shanghai Univ Int Business & Econ, Sch Stat & Informat, Shanghai, Peoples R China
2.Southern Univ Sci & Technol, Dept Math, Shenzhen, Peoples R China
3.City Univ Hong Kong, Dept Math, Hong Kong, Peoples R China
4.Univ Sci & Technol China, Dept Stat & Finance, Hefei 230026, Anhui, Peoples R China

Lin, Hongmei,Jiang, Xuejun,Lian, Heng,et al. Reduced rank modeling for functional regression with functional responses[J]. JOURNAL OF MULTIVARIATE ANALYSIS,2019,169:205-217.

Lin, Hongmei,Jiang, Xuejun,Lian, Heng,&Zhang, Weiping.(2019).Reduced rank modeling for functional regression with functional responses.JOURNAL OF MULTIVARIATE ANALYSIS,169,205-217.

Lin, Hongmei,et al."Reduced rank modeling for functional regression with functional responses".JOURNAL OF MULTIVARIATE ANALYSIS 169(2019):205-217.