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On the estimation of the number of components in multivariate functional principal component analysis.

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Τίτλος: On the estimation of the number of components in multivariate functional principal component analysis.
Συγγραφείς: Golovkine, Steven1 (AUTHOR) steven.golovkine@ul.ie, Gunning, Edward1 (AUTHOR), Simpkin, Andrew J.2 (AUTHOR), Bargary, Norma1 (AUTHOR)
Πηγή: Communications in Statistics: Simulation & Computation. 2026, Vol. 55 Issue 6, p2150-2158. 9p.
Θεματικοί όροι: *Functional analysis, *Simulation methods & models, Principal components analysis, Research methodology evaluation
Περίληψη: Happ and Greven developed a methodology for principal components analysis of multivariate functional data observed on different dimensional domains. Their approach relies on an estimation of univariate functional principal components for each univariate functional feature. In this article, we present extensive simulations to investigate choosing the number of principal components to retain. We show empirically that the conventional approach of using a percentage of variance explained threshold for each univariate functional feature may be unreliable when aiming to explain an overall percentage of variance in the multivariate functional data, and thus we advise practitioners to exercise caution. [ABSTRACT FROM AUTHOR]
Copyright of Communications in Statistics: Simulation & Computation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: On the estimation of the number of components in multivariate functional principal component analysis.
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  Data: <searchLink fieldCode="JN" term="%22Communications+in+Statistics%3A+Simulation+%26+Computation%22">Communications in Statistics: Simulation & Computation</searchLink>. 2026, Vol. 55 Issue 6, p2150-2158. 9p.
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  Data: *<searchLink fieldCode="DE" term="%22Functional+analysis%22">Functional analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Research+methodology+evaluation%22">Research methodology evaluation</searchLink>
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  Data: Happ and Greven developed a methodology for principal components analysis of multivariate functional data observed on different dimensional domains. Their approach relies on an estimation of univariate functional principal components for each univariate functional feature. In this article, we present extensive simulations to investigate choosing the number of principal components to retain. We show empirically that the conventional approach of using a percentage of variance explained threshold for each univariate functional feature may be unreliable when aiming to explain an overall percentage of variance in the multivariate functional data, and thus we advise practitioners to exercise caution. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Communications in Statistics: Simulation & Computation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/03610918.2025.2459862
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      – Code: eng
        Text: English
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        PageCount: 9
        StartPage: 2150
    Subjects:
      – SubjectFull: Functional analysis
        Type: general
      – SubjectFull: Simulation methods & models
        Type: general
      – SubjectFull: Principal components analysis
        Type: general
      – SubjectFull: Research methodology evaluation
        Type: general
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      – TitleFull: On the estimation of the number of components in multivariate functional principal component analysis.
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            – D: 01
              M: 06
              Text: 2026
              Type: published
              Y: 2026
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            – TitleFull: Communications in Statistics: Simulation & Computation
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