Admissible linear estimators of an arbitrary vector of parametric functions in the general Gauss-Markov model
type: Item
instance of: scholarly article
title: Admissible linear estimators of an arbitrary vector of parametric functions in the general Gauss-Markov model
zbMATH Open document ID: 0716.62062
DOI: 10.1016/0378-3758(90)90124-D
author: Jerzy K. Baksalary
author: Augustyn Markiewicz
published in: Journal of Statistical Planning and Inference
publication date: 1990-01-01T00:00:00Z
review text: 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.
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62C15
zbMATH DE Number: 4180579
zbMATH Keywords: minimum variance linear unbiased estimation
zbMATH Keywords: MVLUE
zbMATH Keywords: identifiability
zbMATH Keywords: admissible linear estimation
zbMATH Keywords: general Gauss-Markov model
MaRDI profile type: Publication
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full work available at URL: 0378-3758(90)90124-d
OpenAlex ID: W2029377644
description: scientific article; zbMATH DE number 4180579