  This paper reviews minimax best equivariant estimation in these invariant
estimation problems: a location parameter, a scale parameter and a (Wishart)
covariance matrix. We briefly review development of the best equivariant
estimator as a generalized Bayes estimator relative to right invariant Haar
measure in each case. Then we prove minimaxity of the best equivariant
procedure by giving a least favorable prior sequence based on non-truncated
Gaussian distributions. The results in this paper are all known, but we bring a
fresh and somewhat unified approach by using, in contrast to most proofs in the
literature, a smooth sequence of non truncated priors. This approach leads to
some simplifications in the minimaxity proofs.
