期刊
COMPUTATIONAL STATISTICS & DATA ANALYSIS
卷 55, 期 1, 页码 530-543出版社
ELSEVIER
DOI: 10.1016/j.csda.2010.05.020
关键词
Bayesian; Multiplicative model; PARAFAC; Regularization; Shrinkage
资金
- NSF [SES-0631531]
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. (C) 2010 Elsevier B.V. All rights reserved.
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