Models with a Kronecker product covariance structure: Estimation and testing.

In this article we consider a pq-dimensional random vector x distributed normally with mean vector θ and covariance matrix Λ assumed to be positive definite. On the basis of N independent observations on the random vector x, we want to estimate parameters and test the hypothesis H: Λ = Ψ ⊗ Σ, where Ψ = ( ψ _ij ): q × q, ψ _qq = 1, and Σ = ( σ _ij ): p × p, and Λ = ( ψ _ij Σ), the Kronecker product of Ψ and Σ. That is instead of 1/2 pq( pq + 1) parameters, it has only 1/2 p( p + 1) + 1/2 q( q + 1) − 1 parameters. A test based on the likelihood ratio is given to check if this model holds. And, when this model holds, we test the hypothesis that Ψ is a matrix with intraclass correlation structure. The maximum likelihood estimators (MLE) are obtained under the hypothesis as well as under the alternatives. Using these estimators the likelihood ratio tests (LRT) are obtained. One of the main objects of the paper is to show that the likelihood equations provide unique estimators. [ABSTRACT FROM AUTHOR]