一般Gauss-Markov模型中可估函数的线性Minimax估计
The Linear Minimax Estimators of Estimable Function in a General Gauss-Markov Model
-
摘要: 设Y是具有均值Xβ和协方差阵σ2V的n维随机向量,Sβ是线性可估函数,这里X,S和V≥0是已知矩阵,β∈Rp和σ2>0是未知参数。本文分别在给定的矩阵损失和二次损失下研究了线性估计的Minimax性,在适当的假设下,得到了Sβ的唯一线性Minimax估计(有关唯一性在几乎处处意义下理解)。Abstract: Let Y be a random n-vector with mean Xβ and covariance matrix σ2V, and Sβ be a linear estimable function, where X, S and V≥ 0 are known matrices, β∈ RP and σ2 > 0 are unknown parameters. In this paper under the given matrix loss function and quadratic loss function, the minimax property of linear estimators is studied respectively. Under suitable hypotheses, we obtain the unique linear minimax estimator of Sβ(We must comprehend uniqueness in the sense " almost everywhere ").