An unexpected property of the relative squared error approach to linear regression analysis is derived: It is shown that an estimator being minimax among all linear affine estimators is also minimax in the set of all estimators. Two illustrative special cases are mentioned, where a generalized least squares estimator and a general ridge or Kuks-Olman estimator turn out to be minimax.