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However under the matrix loss (1.2), it is more complicated.
Deng et al. [6] discussed the admissibility under the matrix loss in multivariate model.
[5] derived the BLUP and the admissible predictor under the matrix loss function.
It is invariant under the matrix inversion and congruence transformations, that is, d A, B = d A - 1, B - 1 = d M A M *, M B M * (16).
In this paper, using the methods of linear algebra and matrix theory, we discuss the admissibility of linear estimators in model (1.1) under the matrix loss (1.2).
Under the matrix loss function, Zhu and Lu [4] and Baksalary and Markiewicz [5] studied the admissibility of linear estimators when respectively.
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Let be a closed convex set in and let denote the projection operator onto under the matrix-induced norm, then one has (2.4).
Let be a nonempty closed convex subset of, and let denote the projection mapping from onto, under the matrix-induced norm.
Under ideal conditions, the matrix is diagonalized and the remaining factors can be merged with.
The sections were then dried and stored under vacuum until the matrix was applied.
Under this condition, the matrix C={ c n, k } represents the cluster to which each gene belongs.
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