Hierarchical multilinear models for multiway data

Details Hierarchical multilinear models for multiway data Hierarchical multilinear models for multiway data

2010-05-29

arxiv.org/abs/1005.5425v1

Statistics

Methodology

Scientific paper

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 {\it a priori} independent, but exchangeable. Such a hierarchical approach allows more flexibility in the types of patterns that can be represented.

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