Reduced rank modeling for functional regression with functional responses

JOURNAL OF MULTIVARIATE ANALYSIS

卷:169

Article

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

[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

关键词: Dimension reduction;    Functional data;    Functional response;    Reproducing kernel Hilbert space;   

DOI  :  10.1016/j.jmva.2018.09.004

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free