Common Principal Components for Dependent Random Vectors

Beat E. Neuenschwander and Bernard D. Flury

Journal of Multivariate Analysis, 2000, vol. 75, issue 2, 163-183

Abstract: Let the kp-variate random vector X be partitioned into k subvectors Xi of dimension p each, and let the covariance matrix [Psi] of X be partitioned analogously into submatrices [Psi]ij. The common principal component (CPC) model for dependent random vectors assumes the existence of an orthogonal p by p matrix [beta] such that [beta]t[Psi]ij[beta] is diagonal for all (i, j). After a formal definition of the model, normal theory maximum likelihood estimators are obtained. The asymptotic theory for the estimated orthogonal matrix is derived by a new technique of choosing proper subsets of functionally independent parameters.

Keywords: asymptotic distribution; eigenvalue; eigenvector; entropy; maximum likelihood estimation; multivariate normal distribution; patterned covariance matrices (search for similar items in EconPapers)
Date: 2000
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Citations: View citations in EconPapers (2)

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