On the asymptotic optimality of orthoregressional estimates

It is shown that the orthoregressive (STLS) parameter estimates in simultaneous linear systems (including autonomous difference equations with matrix coefficients) converge to the maximum likelihood estimates and thus become asymptotically best in the limit case of large variances of random coordinates on the manifold of solutions to the system observed with additive random perturbations.