A hierarchical eigenmodel for pooled covariance estimation
By Peter D. Hoff. 2008
62 citations recorded by the source corpus.
Summary. Although the covariance matrices corresponding to different populations are unlikely to be exactly equal they can still exhibit a high degree of similarity. For example, some pairs of variables may be positively correlated across most groups, whereas the correlation between other pairs may be consistently negative. In such cases much of the similarity across covariance matrices can be described by similarities in their principal axes, which are the axes that are defined by the eigenvectors of the covariance matrices. Estimating the degree of across‐population eigenvector heterogeneity can be helpful for a variety of estimation tasks. For example, eigenvector matrices can be pooled to form a central set of principal axes and, to the extent that the axes are similar, covariance estimates for populations having small sample sizes can be stabilized by shrinking their principal axes towards the across‐population centre. To this end, the paper develops a hierarchical model and estimation procedure for pooling principal axes across several populations. The model for the across‐group heterogeneity is based on a matrix‐valued antipodally symmetric Bingham distribution that can flexibly describe notions of ‘centre’ and ‘spread’ for a population of orthogonal matrices.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/29ab2faa0494703688f183b917b5299b1f6c945e
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In Statistics and Probability
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Citations in the corpus
- Covariance Estimation: The GLM and Regularization Perspectives (2011, methodology): “The idea of introducing matrix Bingham distributions as priors on the group of orthogonal matrices (Hoff, 2009) could also be useful in shrinking the eigenvectors of the sample covariance matrix.”