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正态线性试验中可估函数的最小最大估计
On Minimax Estimators of Estimable Funetions in Normal Linear Experiments
【摘要】 对于正态线性试验NL(Xβ,δ2V),V为已知κ×n阶正定矩阵,δ2为未知正参数,通过容许性理论,在平方损失函数(δ2+βrXrV-1Xβ)-1‖δ-SXβ‖下,本文证明了SXβ的线性估计是所有估计类中一致最小最大估计。
【Abstract】 For the normal linear experiment NL( Xβ, σ2 V) where V is a known k × n positive definite matrix, σ2 is a unknown positive paremeter, under qudratic loss function [σ2 +βrXrV-1Xβ-1||δ - SXβ|| , by the theory of admissibility, this paper proves that a linear estimator for SXβ is the unique minimax estimator inb the class of all eatimators.
【关键词】 正态线性试验;
平方损失;
可估线性函数;
最小最大估计;
容许性理论;
【Key words】 normal linear experiment; quadratic loss; estimable linear function; ninimax estimator; admissibility theorey;
【Key words】 normal linear experiment; quadratic loss; estimable linear function; ninimax estimator; admissibility theorey;
- 【文献出处】 菏泽师专学报 ,Journal of Heze Teachers College , 编辑部邮箱 ,1999年04期
- 【分类号】O212.1
- 【被引频次】1
- 【下载频次】14