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Article by |
(\Theta-\Theta_1)'N(\Theta-\Theta_1)\leq\sigma^2$ʱ���Թ��ƵĿ�����������.
�������,
�����о���Լ��$\mbox{tr}(\Theta-\Theta_1)'N(\Theta-\Theta_1)\leq
\sigma^2$��$N$�ͷ����ĵ�$\Theta_1$�����Թ��ƵĿ������Ե�Ӱ��.
��Ҫ���������������ͬ�IJ���ȫ����Լ������$\mbox{tr}(\Theta-\Theta_1)'
N(\Theta-\Theta_1)\leq\sigma^2$��$\mbox{tr}(\Theta-\Theta_2)'
N(\Theta-\Theta_2)\leq\sigma^2$ ��,
��$\Theta_1$��$\Theta_2$����һ���Ĺ�ϵʱ,
�������������Թ���������ͬ��.
linear estimators in multivariate linear models with respect to an
incomplete ellipsoidal restriction
$\mbox{tr}(\Theta-\Theta_1)'N(\Theta-\Theta_1)\leq\sigma^2$.
Specifically, we study the influence of the matrix $N$ and
$\Theta_1$ which is the center of a restricted set to the
admissibility of linear estimators in multivariate linear models
with respect to the incomplete ellipsoidal restriction
$\mbox{tr}(\Theta-\Theta_1)'N(\Theta-\Theta_1)\leq\sigma^2$. The
main results show that the class of admissible linear estimators
with the restriction
$\mbox{tr}(\Theta-\Theta_1)'N(\Theta-\Theta_1)\leq\sigma^2$ is the
same as the one with the restriction
$\mbox{tr}(\Theta-\Theta_2)'N(\Theta-\Theta_2)\leq\sigma^2$ for
$\Theta_1$ and $\Theta_2$ with certain relationship.