1 Introduction
We review some results on minimaxity of best equivariant estimators from what we hope is a fresh and somewhat unified perspective. Our basic approach is to start with a general equivariant estimator, and demonstrate that the best equivariant estimator is a generalized Bayes estimator, , with respect to an invariant prior. We then choose an appropriate sequence of Gaussian priors whose support is the entirety of the parameter space and show that the Bayes risks converge to the constant risk of . This implies that is minimax. All results on best equivariance and minimaxity, which we consider in this paper, are known in the literature. But, using a sequence of Gaussian priors as a least favorable sequence, simplifies the proofs and gives fresh and unified perspective.
In this paper, we consider the following three estimation problems.
 Estimation of a location parameter:

Let the density function of be given by
(1.1) Consider estimation of the location parameter under location invariant loss
(1.2) We study equivariant estimators under the location group, given by
(1.3)  Estimation of a scale parameter:

Let the density function of be given by
(1.4) with scale parameter , where . Consider estimation of the scale under scale invariant loss
(1.5) We study equivariant estimators under scale group, given by
(1.6)  Estimation of covariance matrix:

We study estimation of based on a random matrix having a Wishart distribution , where the density is given in (2.3) below. An estimator is evaluated by the invariant loss
(1.7) We consider equivariant estimators under the lower triangular group, given by
(1.8) where , the set of lower triangular matrices with positive diagonal entries.
For the first two cases with the squared error loss and the entropy loss , respectively, the so called Pitman (1939) estimators
(1.9)  
(1.10) 
are wellknown to be best equivariant and minimax. Clearly, they are generalized Bayes with respect to and , respectively. Girshick and Savage (1951) gave the original proof of minimaxity. Kubokawa (2004)
also gives a proof and further developments in the restricted parameter setting. Both use a sequence of uniform distribution on expanding interval as least favorable priors.
For the last case, James and Stein (1961) show that the best equivariant estimator is given by
(1.11) 
where is from the Cholesky decomposition of and for . Note that the group of lower triangular matrices with positive diagonal entries is solvable, and the result of Kiefer (1957) implies the minimaxity of . Tsukuma and Kubokawa (2015) gives as a sequence of least favorable priors, the invariant prior truncated on a sequence of expanding sets.
In each case, the sequence of priors we employ is based on a Gaussian sequence of possibly transformed parameters. This is in contrast to most proofs in the literature which use truncated versions of the invariant prior. As a consequence, the resulting proofs are less complicated.
Section 2 is devoted to developing the best equivariant estimator as a generalized Bayes estimator with respect to a right invariant (Haar measure) prior in each case. The general approach is basically that of Hora and Buehler (1966). Section 3 provides minimaxity proofs of the best equivariant procedure by giving a least favorable prior sequence based on (possibly transformed) Gaussian priors in each cases. We give some concluding remarks in Section 4.
2 Establishing best equivariant procedures
All results in this section are wellknown. Our proof of best equivariance for , and follow from Hora and Buehler (1966). The reader is referred to Hora and Buehler’s (1966) for further details on their general development of a best equivariant estimator as the generalized Bayes estimator relative to right invariant Haar measure.
2.1 Estimation of location parameter
Consider an equivariant estimator which satisfies . Then we have a following result.
Theorem 2.1.
Proof.
The risk of the equivariant estimator (1.3) is written as
(2.1)  
Then the best equivariant estimator is
∎
2.2 Estimation of scale
Consider an equivariant estimator which satisfies . Then we have a following result.
Theorem 2.2.
Proof.
The risk of the equivariant estimator is written as
(2.2)  
Then the best equivariant estimator is
∎
2.3 Estimation of covariance matrix
Let have a Wishart distribution . Let be the set of lower triangular matrices with positive diagonal entries. By the Cholesky decomposition, and can be written as
for and . As in Theorem 7.2.1 of Anderson (2003)
, the probability density function of
is(2.3) 
where is a normalizing constant given by
(2.4) 
and is the leftinvariant Haar measure on given by
(2.5) 
An estimator
is evaluated by the invariant loss function given by
(2.6) 
Denote the risk function by
For all , the group transformation with respect to on a random matrix and a parameter matrix is defined by . The group operating on is transitive. Any equivariant estimator of
under the lower triangular group is of form given by
(2.7) 
Theorem 2.3.
Let and let the loss be as in (2.6). Then the generalized Bayes estimator with respect to the prior
(2.8) 
, is best equivariant under lower triangular group, that is,
(2.9) 
Note that is the “left” invariant measure, which seems to contradict the general theory by Hora and Buehler (1966). However this seeming anomaly is due to our parameterization , and
(2.10) 
The general theory implies that
(2.11) 
where is right invariant Haar measure on given by
(2.12) 
In the proof below, in addition to the left invariance of , and the right invariance of , we use the fact that
(2.13) 
3 Minimaxity
In this section, we choose an appropriate sequence of priors whose support is the entirety of the parameter space and show that the Bayes risks converge to the constant risk of the best equivariant estimator . By a wellknown standard result (see e.g. Lehmann and Casella (1998)), this implies minimaxity of . In order to deal with explicit expressions for minimax estimators as well as for somewhat technical reasons, in this section, we specify the loss functions to be standard choices in the literature. For the location and scale problem, the squared error loss and the entropy loss
are used respectively. For estimation of covariance matrix, the so called Stein’s (1956) loss function given by
(3.1) 
is used.
3.1 Estimation of location
In this section, we show the minimaxity of , the best location equivariant estimator under squared error loss. A point of departure from most proofs in the literature is that a smooth sequence of Gaussian densities simplifies the proof. It is also easily applied in the multivariate location family (See Remark 3.1).
Recall that the Bayes estimator corresponding to a (generalized) prior , under squared error loss, is given by
(3.2)  
(3.3) 
Hence, by Theorem 2.1, the best equivariant estimator is given by
(3.4) 
Theorem 3.1.
Under the squared error loss, the Bayes estimator is explicitly written as (3.3), However, in the following proof, the implicit expression (3.2) is mainly used to indicate possible extension for more general loss functions. For the same reason, instead of is used.
Proof of Theorem 3.1.
Let
The Bayes risk of under the prior is given by
Also the corresponding Bayes estimator is given by
Clearly
and therefore, to show , it suffices to prove
Making the transformation yields
where
Now, make the transformation . We then have
or equivalently
Hence, by change of variables, we have
Note also and
Since for any , the dominated convergence theorem implies
(3.5) 
and hence
(3.6) 
Hence by Fatou’s lemma, we obtain that
(3.7) 
∎
Remark 3.1.
In the multivariate case, suppose and
Let . Then the Pitman estimator of , the generalized Bayes estimator with respect to , is
(3.8) 
Using
as the least favorable sequence of priors gives minimaxity under the quadratic loss of (3.8).
3.2 Estimation of scale
In this section, we show the minimaxity of the scale Pitman estimator under entropy loss given by
(3.9) 
Recall that the Bayes estimator corresponding to a (generalized) prior , under entropy loss (3.9), is given by
(3.10)  
(3.11) 
Hence the generalized Bayes estimator under , which is best equivariant as shown in Theorem 2.2, is given by
(3.12) 
We have a following minimaxity result.
Theorem 3.2.
Proof.
Assume or equivalently
where is the pdf of . Then the Bayes estimator satisfies
and the Bayes risk is given by
Clearly
and therefore, to show , it suffices to prove
Making the transformation yields
where
Now, make the transformation . We then have
or equivalently
Hence
where and is explicitly given as (when the loss is (3.9))
(3.13) 
Note
Since
for any , the dominated convergence theorem implies
(3.14) 
Also the continuity of implies
(3.15) 
Hence by Fatou’s lemma, we obtain that
(3.16) 
∎
Remark 3.2.
In the same way, we can consider the estimation of with and propose the corresponding result,
is minimax and best equivariant for estimating under entropy loss
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