Structured covariance matrices in multivariate regression models
نویسنده
چکیده
A similarity matrix is a covariance matrix generated by additive nested common factors having independent components. The set of such matrices is a structured subset of covariance matrices, closed under permutation and restriction, which makes it potentially useful as a sub-model for the joint dependence of several responses. It is also equal to the set of rooted trees. Some issues connected with parameter estimation and Bayesian model formulation for such structured sets and subsets are discussed. Although the set of similarity matrices has a rich algebraic structure, the fact that it is not a manifold leads to difficulties in computational work.
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