On the asymptotic optimality of orthoregressional estimators

Resumo

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