  The multivariate errors-in-variables regression model is applicable when both
dependent and independent variables in a multivariate regression are subject to
measurement errors. In such a scenario it is long established that the
traditional least squares approach to estimating the model parameters is biased
and inconsistent. The generalized least squares, ordinary least squares and
maximum likelihood estimators (under the assumption of Gaussian errors) were
derived in the seminal paper of Gleser (1981). However, the ordinary least
squares and maximum likelihood estimators for the mean vectors were incorrectly
derived. In this short paper we amend this error, presenting the correct
estimators of the mean vectors.
