Hierarchical multilinear models for multiway data

Peter D. Hoff

Computational Statistics & Data Analysis, 2011, vol. 55, issue 1, 530-543

Abstract: Reduced-rank decompositions provide descriptions of the variation among the elements of a matrix or array. In such decompositions, the elements of an array are expressed as products of low-dimensional latent factors. This article presents a model-based version of such a decomposition, extending the scope of reduced-rank methods to accommodate a variety of data types such as longitudinal social networks and continuous multivariate data that are cross-classified by categorical variables. The proposed model-based approach is hierarchical, in that the latent factors corresponding to a given dimension of the array are not a priori independent, but exchangeable. Such a hierarchical approach allows more flexibility in the types of patterns that can be represented.

Keywords: Bayesian; Multiplicative; model; PARAFAC; Regularization; Shrinkage (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:55:y:2011:i:1:p:530-543

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