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...

We give an alternative proof of a Theorem by Silvey (1959) for the asymptotic covariance matrix of the maximum likelihood estimator under different types of constraints in the parameter space. We show that such constraints can be classified into three types: constraints that arise from the...