| dc.contributor.author | Stanton, Gray | |
| dc.contributor.author | Ramírez García, David | |
| dc.contributor.author | Santamaría Caballero, Luis Ignacio | |
| dc.contributor.author | Scharf, Louis | |
| dc.contributor.author | Wang, Haonan | |
| dc.contributor.other | Universidad de Cantabria | es_ES |
| dc.date.accessioned | 2024-09-24T08:23:22Z | |
| dc.date.available | 2024-09-24T08:23:22Z | |
| dc.date.issued | 2024-07-12 | |
| dc.identifier.issn | 1053-587X | |
| dc.identifier.issn | 1941-0476 | |
| dc.identifier.other | PID2021-123182OB-I00 | es_ES |
| dc.identifier.other | PID2022-137099NB-C43 | es_ES |
| dc.identifier.uri | https://hdl.handle.net/10902/33946 | |
| dc.description.abstract | Recent work (Ramírez et al. 2020) has introduced Multi-Channel Factor Analysis (MFA) as an extension of factor analysis to multi-channel data that allows for latent factors common to all channels as well as factors specific to each channel. This paper validates the MFA covariance model and analyzes the statistical properties of the MFA estimators. In particular, a thorough investigation of model identifiability under varying
latent factor structures is conducted, and sufficient conditions forgeneric global identifiability of MFA are obtained. The develop ment of these identifiability conditions enables asymptotic analy sis of estimators obtained by maximizing a Gaussian likelihood, which are shown to be consistent and asymptotically normal even under misspecification of the latent factor distribution. | es_ES |
| dc.description.sponsorship | The authors thank the reviewers for their constructive comments. This work was supported in part by National Science Foundation grants DMS-1923142, CNS-1932413, and DMS-2123761. The work of I. Santamaria was funded by AEI /10.13039/501100011033 and FEDER UE under grant PID2022-137099NB-C43 (MADDIE). The work of D. Ramírez was partially supported by MICIU/AEI/10.13039/501100011033/FEDER, UE, under grant PID2021-123182OB-I00 (EPiCENTER), by the Office of Naval Research (ONR) Global under contract N62909-23-1-2002, and by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEU through the UNICO 5G I+D SORUS project. | es_ES |
| dc.format.extent | 16 p. | es_ES |
| dc.language.iso | eng | es_ES |
| dc.publisher | Institute of Electrical and Electronics Engineers, Inc. | es_ES |
| dc.rights | © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | es_ES |
| dc.source | IEEE Transactions on Signal Processing, 2024, 72, 3562-3577 | es_ES |
| dc.subject.other | Asymptotic normality | es_ES |
| dc.subject.other | Consistency | es_ES |
| dc.subject.other | Factor analysis (FA) | es_ES |
| dc.subject.other | Identifiability | es_ES |
| dc.subject.other | Multi-channel factor analysis (MFA) | es_ES |
| dc.title | Multi-channel factor analysis: identifiability and asymptotics | es_ES |
| dc.type | info:eu-repo/semantics/article | es_ES |
| dc.relation.publisherVersion | https://doi.org/10.1109/TSP.2024.3427004 | es_ES |
| dc.rights.accessRights | openAccess | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-123182OB-I00/ES/MODELOS PROFUNDOS Y EXPLICABLES BASADOS EN VARIABLES LATENTES PARA SALUD MENTAL/ | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-137099NB-C43/ES/TECNOLOGIAS DE COMUNICACION, CODIFICACION Y PROCESADO PARA REDES CLASICAS-CUANTICAS DE PROXIMA GENERACION/ | es_ES |
| dc.identifier.DOI | 10.1109/TSP.2024.3427004 | |
| dc.type.version | acceptedVersion | es_ES |
