General admissibility for linear estimators of multivariate random regression coefficients and parameters with respect to a restricted parameter set |
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Authors: | Ai-ling Xiao Zhi-zhong Wang |
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Institution: | [1]School of Info-physics and Geometics Engineering, Central South University, Changsha 410083, China [2]School of Informeties, Guangdong University of Foreign Studies, Guangzhou 510006, China [3]School of Mathematica Science and Computing Technology, Central South University, Changsha 410083, China |
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Abstract: | This paper considers the linear model effected by random disturbance, Y = XB + ɛ, where $
\left \begin{gathered}
B \hfill \\
\varepsilon \hfill \\
\end{gathered} \right] \sim \left( {\left \begin{gathered}
A\Theta \hfill \\
0 \hfill \\
\end{gathered} \right],V \otimes \Sigma } \right)
$
\left \begin{gathered}
B \hfill \\
\varepsilon \hfill \\
\end{gathered} \right] \sim \left( {\left \begin{gathered}
A\Theta \hfill \\
0 \hfill \\
\end{gathered} \right],V \otimes \Sigma } \right)
, and Θ
T
A
T
X
T
NXAΘ ⩽ Σ. It gives a definition for general admissible estimator of a linear function SΘ + GB of random regression coefficients and parameters. The necessary and sufficient conditions for LY and LY + C to be general admissible estimators of SΘ + GB in the class of both homogenous and non-homogenous linear estimators are obtained. The conclusion is not dependent of whether
or not SΘ +GB is estimable. |
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Keywords: | parametric matrix linear estimator general optimality general admissibility |
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