Academic Journal
Bayesian multi-group functional factor models with parameter-expanded cumulative shrinkage priors.
| Τίτλος: | Bayesian multi-group functional factor models with parameter-expanded cumulative shrinkage priors. |
|---|---|
| Συγγραφείς: | Dai, Xuanye1 (AUTHOR), Gottard, Anna1 (AUTHOR) anna.gottard@unifi.it, Guindani, Michele2 (AUTHOR), Vannucci, Marina3 (AUTHOR) |
| Πηγή: | Statistical Methods & Applications. Jun2026, p1-27. 27p. |
| Θεματικοί όροι: | *Functional analysis, Dimensional reduction algorithms |
| Περίληψη: | Functional data consist of trajectories observed over a continuous domain, such as time, space, or wavelength. Here we consider curves observed on different groups of subjects and propose a Bayesian multi-group functional factor analysis framework that jointly models the data via an explicit decomposition into group-specific mean functions and latent components that capture both common and distinct latent structures across the groups. We represent these functional components as linear combinations of a common set of B-spline bases, achieving a low-rank representation of the latent factors. We further impose a parameter-expanded cumulative shrinkage process prior on the factor loadings, which induces increasing shrinkage and automatically selects the number of active shared and group-specific factors. We evaluate the model’s performance through simulation studies and show that the model accurately recovers the number of underlying factors and effectively distinguishes variations in functional observations driven by shared versus group-specific complex structures under various scenarios. For real data analysis, we apply the model to EEG data on alcoholic and healthy subjects and identify shared latent factors, that capture canonical characteristic components of the EEG curves, along with group-specific factors that reveal specific neural activity patterns. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Business Source Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Bayesian multi-group functional factor models with parameter-expanded cumulative shrinkage priors. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dai%2C+Xuanye%22">Dai, Xuanye</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gottard%2C+Anna%22">Gottard, Anna</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> anna.gottard@unifi.it</i><br /><searchLink fieldCode="AR" term="%22Guindani%2C+Michele%22">Guindani, Michele</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Vannucci%2C+Marina%22">Vannucci, Marina</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Statistical+Methods+%26+Applications%22">Statistical Methods & Applications</searchLink>. Jun2026, p1-27. 27p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Functional+analysis%22">Functional analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Dimensional+reduction+algorithms%22">Dimensional reduction algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Functional data consist of trajectories observed over a continuous domain, such as time, space, or wavelength. Here we consider curves observed on different groups of subjects and propose a Bayesian multi-group functional factor analysis framework that jointly models the data via an explicit decomposition into group-specific mean functions and latent components that capture both common and distinct latent structures across the groups. We represent these functional components as linear combinations of a common set of B-spline bases, achieving a low-rank representation of the latent factors. We further impose a parameter-expanded cumulative shrinkage process prior on the factor loadings, which induces increasing shrinkage and automatically selects the number of active shared and group-specific factors. We evaluate the model’s performance through simulation studies and show that the model accurately recovers the number of underlying factors and effectively distinguishes variations in functional observations driven by shared versus group-specific complex structures under various scenarios. For real data analysis, we apply the model to EEG data on alcoholic and healthy subjects and identify shared latent factors, that capture canonical characteristic components of the EEG curves, along with group-specific factors that reveal specific neural activity patterns. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Statistical Methods & Applications is the property of Springer Nature 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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10260-026-00861-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 1 Subjects: – SubjectFull: Functional analysis Type: general – SubjectFull: Dimensional reduction algorithms Type: general Titles: – TitleFull: Bayesian multi-group functional factor models with parameter-expanded cumulative shrinkage priors. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dai, Xuanye – PersonEntity: Name: NameFull: Gottard, Anna – PersonEntity: Name: NameFull: Guindani, Michele – PersonEntity: Name: NameFull: Vannucci, Marina IsPartOfRelationships: – BibEntity: Dates: – D: 16 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16182510 Titles: – TitleFull: Statistical Methods & Applications Type: main |
| ResultId | 1 |