Latent models for cross-covariance

Authors:
Jacob A. Wegelin;Asa Packer;Thomas S. Richardson
Affiliations:
Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of California, Davis, One Shields Avenue, MS 1-C, Davis, CA 95616, USA;BioSonics, Inc., 4027 Leary Way NW, Seattle, WA 98107, USA;University of Washington, Department of Statistics, Box 354322, Seattle, WA 98195, USA
Venue:
Journal of Multivariate Analysis
Year:
2006

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Abstract

We consider models for the covariance between two blocks of variables. Such models are often used in situations where latent variables are believed to present. In this paper we characterize exactly the set of distributions given by a class of models with one-dimensional latent variables. These models relate two blocks of observed variables, modeling only the cross-covariance matrix. We describe the relation of this model to the singular value decomposition of the cross-covariance matrix. We show that, although the model is underidentified, useful information may be extracted. We further consider an alternative parameterization in which one latent variable is associated with each block, and we extend the result to models with r-dimensional latent variables.