Reduced-rank models for interaction in unequally replicated two-way classifications
Journal of Multivariate Analysis
Multiway data analysis
Rank, decomposition, and uniqueness for 3-way and n-way arrays
Multiway data analysis
Admissibility and minimaxity of Bayes estimators for a normal mean matrix
Journal of Multivariate Analysis
Generalized Bayes minimax estimation of the normal mean matrix with unknown covariance matrix
Journal of Multivariate Analysis
A comparison of algorithms for fitting the PARAFAC model
Computational Statistics & Data Analysis
A combined overdispersed and marginalized multilevel model
Computational Statistics & Data Analysis
The multilinear normal distribution: Introduction and some basic properties
Journal of Multivariate Analysis
| Hi-index | 0.03 |
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.