Multi-channel factor analysis: Identifiability and asymptotics Articles uri icon

authors

publication date

  • July 2024

start page

  • 3562

end page

  • 3577

volume

  • 72

International Standard Serial Number (ISSN)

  • 1053-587X

Electronic International Standard Serial Number (EISSN)

  • 1941-0476

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 for generic global identifiability of MFA are obtained. The development of these identifiability conditions enables asymptotic analysis 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.

subjects

  • Telecommunications

keywords

  • asymptotic normality; consistency; factor analysis (fa); identifiability; multi-channel factor analysis (mfa)