- Research Article
- Open Access
Several Matrix Euclidean Norm Inequalities Involving Kantorovich Inequality
© L.Wang and H. Yang. 2009
Received: 21 April 2009
Accepted: 4 August 2009
Published: 26 August 2009
Kantorovich inequality is a very useful tool to study the inefficiency of the ordinary least-squares estimate with one regressor. When regressors are more than one statisticians have to extend it. Matrix, determinant, and trace versions of it have been presented in the literature. In this paper, we provide matrix Euclidean norm Kantorovich inequalities.
To the best of our knowledge, there has not been any matrix Euclidean norm version of Kantorovich inequality yet. Our goal is to present its matrix Euclidean norm version.
This paper is arranged as follows. In Section 2, we will give some lemmas which are useful in the following section. In Section 3, some matrix inequalities are established by Kantorovich inequality or Pólya-Szegö inequality, which are referred to as the extensions of Kantorovich inequality as well and conclusions are given in Section 4.
2. Some Lemmas
We will start with some lemmas which are very useful in the following.
Lemma 2.3 (Pólya-Szegö inequality).
Moreover Greub and Rheinboldt  generalized Pólya-Szegö inequality to matrices.
Lemma 2.4 (Poincare).
3. Main Results
See also .
In Theorem 3.1, we need the assumption that . However, we should also point out that the matrix may not meet such an assumption in practice. Therefore, we relax this assumption in the following but the results are weaken.
The proof is completed.
It is very easy to prove therefore we omit the proof.
This completes the proof.
This completes the proof.
The study of the inefficiency of the ordinary least-squares estimator in the linear model requires a lower bound for the efficiency defined as the ratio of the variance or covariance of the best linear unbiased estimator to the variance or covariance of the ordinary least-squares estimator. Such a bound can be given by Kantorovich inequality or its extensions. Matrix, determinant, and trace versions of it have been presented in the literature. In this paper, we present its matrix Euclidean norm version.
The authors thank very much the associate editors and reviewers for their insightful comments and kind suggestions that lead to improving the presentation.
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