A hierarchical eigenmodel for pooled covariance estimation

Statistics – Methodology

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

21 pages, 5 figures, 2 tables

Scientific paper

While a set of 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, while 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, the axes 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. 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 center. To this end, this article 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 ``center'' and ``spread'' for a population of orthonormal matrices.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

A hierarchical eigenmodel for pooled covariance estimation does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with A hierarchical eigenmodel for pooled covariance estimation, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A hierarchical eigenmodel for pooled covariance estimation will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-216220

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.