Nonlinear functional canonical correlation analysis via distance covariance

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

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Detail(s)

Original languageEnglish
Article number104662
Number of pages15
Journal / PublicationJournal of Multivariate Analysis
Volume180
Online published29 Jul 2020
Publication statusPublished - Nov 2020

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DOI

DOI
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Link to Scopus https://www.scopus.com/record/display.uri?eid=2-s2.0-85089011762&origin=recordpage
Permanent Link https://scholars.cityu.edu.hk/en/publications/publication(369a8192-6fe2-4e2d-97b6-cdadadd37697).html

Abstract

Functional canonical correlation analysis (FCCA) is a tool for exploring the associations between a pair of functional data. However, when the association is nonlinear or even nonmonotone, FCCA can fail to discover any meaningful relationship between the pair. In this paper, nonlinear FCCA estimators are constructed based on some popular measures of dependence — distance covariance and distance correlation. Consistency of the estimators is shown. Numerical studies are presented that demonstrate nonlinear FCCA can uncover new association patterns between functional covariates.

Research Area(s)

  • Canonical correlation analysis, Distance correlation, Distance covariance, Statistical consistency

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Publication fingerprints text

100
Covariance Mathematics
100
Canonical Correlati... Mathematics
20
Numerical Analysis Mathematics
20
Covariate Mathematics
20
Functional Data Mathematics
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Citation Format(s)

[ RIS ] [ BibTeX ]
Nonlinear functional canonical correlation analysis via distance covariance. / Zhu, Hanbing; Li, Rui; Zhang, Riquan et al.
In: Journal of Multivariate Analysis, Vol. 180, 104662, 11.2020.

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review