Abstrakt
Linear relations, containing measurement errors in input and output data, are considered. Parameters of these errors-in-variables (EIV) models can be estimated by minimizing the total least squares (TLS) of the input-output disturbances, i.e., penalizing the orthogonal squared misfit.
This approach corresponds to minimizing the Frobenius norm of the error matrix. An extension of the traditional TLS estimator in the EIV model - the EIV estimator - is proposed in the way that a general unitarily invariant norm of the error matrix is minimized.
Such an estimator is highly non-linear. Regardless of the chosen unitarily invariant matrix norm, the corresponding EIV estimator is shown to coincide with the TLS estimator.
Its existence and uniqueness is discussed. Moreover, the EIV estimator is proved to be scale invariant, interchange, direction, and rotation equivariant.