Latent models for cross-covariance

Jacob A. Wegelin, Asa Packer and Thomas S. Richardson

Journal of Multivariate Analysis, 2006, vol. 97, issue 1, 79-102

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.

Keywords: Canonical; correlation; Latent; variables; Partial; least; squares; Reduced-rank; regression; Singular; value; decomposition (search for similar items in EconPapers)
Date: 2006
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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