In the previous chapter, we considered the multivariate linear regression model
In the model, the
n by p matrix Y contains the random observations on p dependent variables,
k + 1 by p matrix B is the matrix of unknown parameters,
n by p matrix ε is the matrix of random errors such that each row of ε is a p variate normal vector with mean vector zero and variance-covariance matrix Σ. The matrix Σ is assumed to be a p by p positive definite matrix.
n by k + 1 matrix X was assumed to be of full rank, that is, Rank(X) = k + 1.
There are, however, situations especially those involving the analysis of classical experimental designs ...