@article{10902/33946,
year = {2024},
month = {7},
url = {https://hdl.handle.net/10902/33946},
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.},
organization = {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.},
publisher = {Institute of Electrical and Electronics Engineers, Inc.},
publisher = {IEEE Transactions on Signal Processing, 2024, 72, 3562-3577},
title = {Multi-channel factor analysis: identifiability and asymptotics},
author = {Stanton, Gray and Ramírez García, David and Santamaría Caballero, Luis Ignacio and Scharf, Louis and Wang, Haonan},
}