Reduced rank modeling for functional regression with functional responses
type: Item
instance of: scholarly article
title: Reduced rank modeling for functional regression with functional responses
zbMATH Open document ID: 1432.62229
author: Hongmei Lin
author: Heng Lian
author: Weiping Zhang
author: Xuejun Jiang
published in: Journal of Multivariate Analysis
publication date: 2019-01-04T00:00:00Z
review text: The paper under review deals with regression problems where both the predictor and the response are functional in nature. The authors 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 model is more parsimonious and allows interpreting the estimated coefficients more easily than in general functional linear regression models. The paper is organized as follows. In section 2, the authors discuss the estimation approach of the functional reduced rank regression model and obtain the convergence rate of the estimator. They further discuss the effect of discretization in implementation and theory. In section 3 simulation studies and real data illustrations are contained. The paper is concluded with a discussion in section 4.
Mathematics Subject Classification ID: 62J12
Mathematics Subject Classification ID: 62G05
Mathematics Subject Classification ID: 62H25
Mathematics Subject Classification ID: 46E22
zbMATH DE Number: 6997753
zbMATH Keywords: dimension reduction
zbMATH Keywords: functional data
zbMATH Keywords: functional response
zbMATH Keywords: reproducing kernel Hilbert space
reviewed by: Joseph Melamed
describes a project that uses: fda (R)
MaRDI profile type: Publication
full work available at URL: j.jmva.2018.09.004
OpenAlex ID: W2889618694
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Wikidata QID: Q129210684
DOI: 10.1016/J.JMVA.2018.09.004
description: scientific article; zbMATH DE number 6997753