Admissible linear estimators of an arbitrary vector of parametric functions in the general Gauss-Markov model
Some results about admissible linear estimation are obtained under the general Gauss-Markov model \(\{\) Y, \(X\beta\), \(\sigma\) \({}^ 2V\}\), with matrix X and V possibly deficient in rank. The author extends the admissible linear estimation, relaxing the assumption that the vector \(K\beta\) is identifiable, with K an \(m\times n\) real matrix. A general characterization is derived, of the class of admissible estimators for \(K\beta\) and \(\beta\). Finally, the admissibility of several estimators of \(\beta\) proposed in the literature is examined.
minimum variance linear unbiased estimation
scientific article; zbMATH DE number 4180579
scientific article