Let denote the class of homogeneous linear estimators, and let denote the class of general linear estimators.
Lemma 2.1.
Under model (1.1) with the loss function (1.2), suppose is an estimator of , one has
The equality holds if and only if
where .
Proof.
Since
It is easy to verify that (2.1) holds, and the equality holds if and only if
Expanding it, we have
Thus , that is .
Lemma 2.2.
Under model (1.1) with the loss function (1.2), if , suppose are estimators of , then is better than if and only if
and the two equalities above cannot hold simultaneously.
Proof.
Since , , , (2.3) implies the sufficiency is true. Suppose is better than , then for any , we have
and there exists some such that the equality in (2.8) cannot hold. Taking in (2.8), (2.6) follows. Let , is the identity matrix, then for any , , by (2.8), we have
Therefore, (2.7) holds. It is obvious that the two equalities in (2.6) and (2.7) cannot hold simultaneously.
Consider univariate linear model with respect to restricted parameter set:
and the loss function
where , , and are as defined in (1.1) and (1.2), and are unknown parameters. Set . If is an admissible estimator of , we denote it by .
Similarly to Lemma 2.2, we have the following lemma.
Lemma 2.3.
Under model (2.10) with the loss function (2.11), suppose and are estimators of , then is better than if and only if
and the two equalities above cannot hold simultaneously.
Theorem 2.4.
Consider the model (1.1) with the loss function (1.2), if and only if in model (2.10) with the loss function (2.11).
Proof.
From Lemmas 2.2 and 2.3, we need only to prove the equivalence of (2.7) and (2.13).
Suppose (2.7) is true, we can take , , and plug it into (2.7). Then (2.13) follows.
For the inverse part, suppose (2.13) is true, let , we have
The claim follows.
Remark 2.5.
From this Theorem, we can easily generalize the result under univariate linear model to the case under multivariate linear model in the class of homogeneous linear estimators.
Theorem 2.6.
Consider the model (1.1) with the loss function (1.2), if is estimable, then if and only if:
(1),
(2)if there exists , such that
then , , where .
Proof.
From the corresponding theorem in article Deng and Chen [13], under the model (2.10) with the loss function (2.11), if is estimable, then if and only if (1) and (2) in Theorem 2.6 are satisfied. Now Theorem 2.6 follows from Theorem 2.4.
Lemma 2.7.
Consider the model (1.1) with the loss function (1.2), suppose is an estimator of . One has
and the equality holds if and only if .
Proof.
The proof follows from the following equalities:
Lemma 2.8.
Assume , one has
(1)if and , then there exists , for every , and .
(2) if and only if for any vector , implies .
Proof.

(1)
If , the claim is trivial. If , , where is an orthogonal matrix, , . From , we have , notice that , we get , where . Clearly, there exists , such that . Let , then , and for every , , thus and .

(2)
The claim is easy to verify.
Theorem 2.9.
Consider the model (1.1) with the loss function (1.2), if is estimable, then if and only if:
(1),
(2)if there exists such that
then , and , where .
Proof.
If , by (2.17) we obtain . Then implies . The claim is true by Theorem 2.6. Now we assume .
Necessity
Assume , by Lemma 2.7, (1) is true. Now we will prove (2). Denote , . Since , rewrite (2.18) as the following
If there exists such that (2.19) holds, for sufficient small , take . Since
Thus
In the above, is sufficiently small, , thus (2.23) follows. , , , thus (2.24) follows
For any compatible vector , assume
By (2.19) we obtain , that is, , plug it into (2.26), then , , thus
From Lemma 2.8, we have
Therefore, there exists , for , the right side of (2.25) is nonnegative definite and its rank is . If is small enough, for every , we have , and the equality cannot always hold if (2) does not hold. It contradicts .
Sufficiency
Assume (1) and (2) are true. Since , by Theorem 2.6, . If there exists an estimator that is better than , then for every ,
Note that for any , if , then . Replace and in (2.29) with and , respectively, divide by on both sides, and let , we get
Since , we have and (otherwise, is better than ). Plug them into (2.29), for every ,
Thus and . implies , and the equality in (2.29) holds always. It contradicts that is better than .
Theorem 2.10.
Under model (1.1) and the loss function (1.2), if is estimable, then if and only if .
Proof.
Denote , , model (1.1) is transformed into
Since
then (2.33) implies that , which combining Theorem 2.4 and the fact that "if , then " yields .
Corollary 2.11.
Under model (1.1) and the loss function (1.2), if is estimable, then if and only if .
Lemma 2.12.
Consider model (1.1) with the loss function (1.2), suppose , if , then
Proof.
where refers to the orthogonal projection onto .
Lemma 2.13.
Suppose and are and real matrices, respectively, there exists a matrix such that if and only if and .
Proof.
For the proof of sufficiency, we need only to prove that there exists a such that is not an inverse symmetric matrix.
Since , .
(1)If there is such that , take , then
where is the column vector whose only nonzero entry is a 1 in the th position.
(2)If there does not exist such that , then there must exist such that and , take , then
That is, .
The proof is complete.
Theorem 2.14.
Consider the model (1.1) with the loss function (1.2), if is inestimable, then if and only if .
Proof.
Lemma 2.1 implies the necessity. For the proof of the inverse part, assume there exists , for any , we have
Since
where , thus
where is a known function. If there exists such that
note that is inestimable, then , by Lemma 2.13, there exists such that
Take , , since , so .
According to (2.40), we have for any real ,
It is a contradiction. Therefore . Since , by Lemma 2.12, we obtain
Take in (2.38), we have
Thus , . There is no estimator that is better than in .
Similarly to Theorem 2.14, we have the following theorem.
Theorem 2.15.
Under model (1.1) and the loss function (1.2), if is inestimable, then if and only if .
Remark 2.16.
This theorem indicates that if is inestimable, then the admissibility of has no relation with the choice of owing to .